Error vector decomposition based augmented reality prescription lens module calibration compensation system

Abstract

The application discloses an augmented reality prescription lens module calibration compensation system based on error vector decomposition, which comprises a display module for projecting a virtual image, a prescription lens module comprising a transparent substrate, a diffractive microstructure layer and a multilayer antireflection film system, a wavefront sensor for detecting total wavefront error, an infrared detection module for detecting the transmittance and scattering intensity of the prescription lens module in the infrared band, and a control unit for decomposing the total optical path error of the system into a display module inherent error vector and a prescription lens introduced error vector based on the total wavefront error, generating dynamic pre-compensation parameters according to the prescription lens introduced error vector, and pre-distorting the projected image; and the optical fingerprint data packet is stored and managed simultaneously; the application realizes the joint optimization of display distortion and eye movement tracking infrared performance, and has the characteristics of personalized dynamic compensation, plug and play and data traceability.

Description

Technical Field

[0001] This invention belongs to the field of augmented reality (AR) display and optical compensation technology, and specifically discloses an augmented reality prescription lens module calibration and compensation system based on error vector decomposition. Background Technology

[0002] To accommodate the refractive errors of different users, augmented reality display devices typically employ a method of attaching prescription lenses in front of the display optical engine, such as clip-on lenses, magnetic lenses, or custom lenses embedded in the optical path. This design allows users to obtain a clear visual experience that blends reality and virtuality when using AR glasses, while retaining their original vision correction habits. However, existing technologies have the following shortcomings: Secondary optical path interference: Prescription lenses not only correct the refractive power of the human eye when viewing the real scene, but also cause unexpected refraction of the virtual image light emitted by the display module, resulting in distortion, astigmatism and chromatic aberration of the virtual image; the distortion correction of traditional AR glasses is based on the standard naked-eye model calibration before leaving the factory, and it becomes ineffective after prescription lenses are added. Static compensation failure: Different lenses introduce different errors, and even lenses of the same power vary due to manufacturing tolerances. Current technology cannot achieve personalized dynamic compensation. Impaired eye-tracking performance: Modern AR glasses often integrate eye-tracking functionality, commonly using 850nm or 940nm infrared light sources. If prescription lenses absorb or scatter excessively in the infrared band, it will reduce the signal-to-noise ratio of corneal reflection and pupil imaging, leading to tracking errors. Furthermore, current lens designs often only focus on visible light transmittance, neglecting infrared compatibility. Error sources are difficult to separate: Traditional methods treat the entire optical path as a black box for overall calibration, which makes it impossible to distinguish whether the distortion comes from the design tolerance of the display module itself or from the physical introduction of the prescription lens, resulting in low compensation accuracy and long debugging cycle; To address the aforementioned issues, this invention proposes an augmented reality prescription lens module calibration and compensation system based on error vector decomposition. By decomposing the total optical path error into an inherent error vector of the display module and an error vector introduced by the prescription lens, and integrating an infrared-compatible film system and a diffraction microstructure at the lens level, the system achieves joint optimization of display distortion and eye-tracking performance. Summary of the Invention

[0003] To overcome the aforementioned deficiencies in the prior art, this invention provides an augmented reality prescription lens module calibration and compensation system based on error vector decomposition. By constructing an error vector decomposition model, a wavefront sensor detects the total optical path error and decomposes it into an inherent error vector of the display module and an error vector introduced by the prescription lens. An infrared detection module measures the transmittance and scattering intensity of the prescription lens module in the infrared band. The control unit generates dynamic pre-compensation parameters based on the error vector introduced by the prescription lens, performs pre-distortion processing on the displayed image, and packages the error vector and infrared detection data into a unique optical fingerprint data package for storage and management, effectively solving the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: an augmented reality prescription lens module calibration and compensation system based on error vector decomposition, comprising: Display module, used to project virtual images; A prescription lens module is detachably mounted on the light-emitting side of the display module. The prescription lens module includes a transparent substrate, a diffraction microstructure layer formed on the substrate, and a multilayer antireflective film system covering the diffraction microstructure layer. A wavefront sensor, positioned at the exit pupil position within the eye movement range, is used to detect the total wavefront error after passing through the display module and the prescription lens module; An infrared detection module, comprising an infrared light source and an infrared camera, is used to detect the transmittance and scattering intensity of the prescription lens module in the infrared band. The control unit is communicatively connected to the display module, wavefront sensor, and infrared detection module. The control unit is configured as follows: Based on the total wavefront error, the total optical path error of the system is decomposed into the inherent error vector of the display module and the error vector introduced by the prescription lens through the error vector decomposition model. Dynamic pre-compensation parameters are generated based on the error vector introduced by the prescription lens; The dynamic pre-compensation parameters are loaded into the display module to perform pre-distortion processing on the projected virtual image; Store and manage optical fingerprint data packets containing the error vector introduced by the prescription lens and infrared detection data.

[0005] An electronic device includes a processor and a memory, the memory storing a computer program that, when executed by the processor, performs the following steps: Obtain the total wavefront error detected by the wavefront sensor; The pre-built and calibrated error vector decomposition model is invoked to decompose the total wavefront error into the inherent error vector of the display module and the error vector introduced by the prescription lens. Dynamic pre-compensation parameters are generated based on the error vector introduced by the prescription lens; The prescription lens is introduced into an error vector and packaged with infrared detection data to generate an optical fingerprint data package; The dynamic pre-compensation parameters are invoked to perform pre-distortion processing on the displayed image.

[0006] A computer-readable storage medium storing a computer program that can be loaded by a processor and executed in accordance with the steps described above.

[0007] The technical effects and advantages of this invention are as follows: 1. This invention integrates a diffraction microstructure layer and a multilayer antireflective coating system into a prescription lens module. Through the joint optimization design of the thin film transmission matrix and scattering budget, the lens maintains high transmittance (≥90%) in the visible light band, while also achieving high transmittance (≥85%) and low scattering (haze ≤1%) in the 850nm and 940nm infrared bands commonly used for eye tracking. The infrared detection module can measure the infrared transmittance and scattering intensity of the lens in real time and incorporate the detection results into the optical fingerprint data packet to ensure that each lens meets the signal-to-noise ratio threshold required for eye tracking. This design effectively solves the problem of decreased signal-to-noise ratio of corneal reflection images and increased tracking errors caused by excessive infrared absorption or scattering in traditional prescription lenses, providing hardware assurance for high-precision eye tracking. 2. This system generates a unique optical fingerprint data package for each prescription lens (including the prescription lens error vector, infrared performance data, lens ID, design version, manufacturing batch, and anti-tamper verification information, etc.), realizing digital characterization of lens-level optical characteristics. When a user changes to a prescription lens with a different power, the control unit automatically reads the optical fingerprint data package of the new lens, dynamically updates the pre-compensation parameters, and loads them into the display module without manual intervention or recalibration. This plug-and-play mechanism allows the same AR glasses to adapt to the personalized prescription needs of different users, greatly improving the versatility of the device and the user experience, while reducing the complexity of the fitting process. 3. This invention constructs an error vector decomposition model to accurately decompose the total optical path error of the system into the inherent error vector of the display module and the error vector introduced by the prescription lens. The two are decoupled through a pre-calibrated coupling matrix, allowing for the extraction of the Zernike aberration coefficients (including defocus, astigmatism, and higher-order aberrations) introduced by each personalized prescription lens. Based on this precise error vector, the system inversely calculates and generates a pixel displacement matrix or phase modulation amount, performing pre-distortion processing at the image source level. This effectively counteracts the unintended refraction effects of the prescription lens on the virtual image optical path. Compared to existing technologies that treat the entire optical path as a black box for overall calibration, the error decomposition method of this invention significantly improves compensation accuracy, enabling the residual wavefront error of the virtual image after adding the prescription lens to be controlled within 0.05λ, and geometric distortion to be less than 0.5%, thus improving the user's visual experience. Attached Figure Description

[0008] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0009] Figure 1 This is a diagram of the overall system architecture of the present invention.

[0010] Figure 2 This is a flowchart of the infrared detection process of the present invention.

[0011] Figure 3 This is a flowchart illustrating the plug-and-play lens replacement logic of the present invention. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0014] I. System Overall Architecture This invention provides, for example Figure 1 The augmented reality prescription lens module calibration and compensation system shown includes: The display module is used to project virtual images and includes an optical engine, waveguide sheet and driving circuit. A prescription lens module, detachably mounted on the light-emitting side of the display module, is used to correct the user's refractive power. It includes a transparent substrate, a diffraction microstructure layer formed on the substrate, and a multilayer antireflective coating system covering the diffraction microstructure layer. A wavefront sensor, positioned at the exit pupil position within the eye movement range, is used to detect the total wavefront error after passing through the display module and the prescription lens module; An infrared detection module, comprising an infrared light source and an infrared camera, is used to detect the transmittance and scattering intensity of the prescription lens module in the infrared band. The control unit is communicatively connected to the display module, wavefront sensor, and infrared detection module, and is configured to execute the calibration compensation method of the present invention.

[0015] The specific implementation methods of each module will be described in detail in the following embodiments.

[0016] II. Detailed Implementation of the Display Module In a preferred embodiment of the present invention, the display module uses a high-resolution Micro OLED display as the image source, specifically the Sony ECX339A model, which has a resolution of 1920×1080, a pixel size of 4.5μm, and a refresh rate of 90Hz. This display is connected to the driving circuit via a MIPI interface, and the driving circuit is controlled by the control unit.

[0017] The projection optics system employs a 3-element plastic aspherical lens design, with a total length of 12mm, an exit pupil diameter of 8mm, and an exit pupil distance of 18mm. The waveguide is a Dispelix diffractive waveguide with a field of view of 40° and a luminous efficacy greater than 300cd / lm.

[0018] Before leaving the factory, the display module undergoes basic calibration, including brightness uniformity correction and color correction, to ensure that the projected virtual image has uniform brightness and accurate color when no prescription lens is used. The calibration program built into the display module can output calibration patterns such as uniform brightness field and grid dot matrix adapted to the wavefront sensor, ensuring the accuracy of wavefront detection and distortion calibration.

[0019] III. Specific Implementation Methods of Prescription Lens Modules 3.1 Structural Composition The prescription lens module is detachably mounted on the light-emitting side of the display module and secured with a magnetic structure for easy user replacement. The lens module features a three-layer core structure arranged sequentially along the light path (from the display module to the user's eye): Refractive power shaping surface and multilayer antireflective coating system: Located on the display side surface of the transparent substrate (away from the human eye), it is formed by precision optical mold to achieve user-customized spherical power, cylindrical power and axis. The surface is coated with a visible light-infrared compatible multilayer antireflective coating system to avoid the coating layer affecting the diffraction microstructure on the human eye side. Transparent substrate: Made of high-transparency polycarbonate (PC) material with a refractive index of 1.58 at 550 nm, an Abbe number of 58, a thickness of 2.0 mm, and a diameter of 50 mm. Both sides of the substrate are optically precision-molded surfaces, respectively supporting the refractive structure and the diffraction microstructure. Diffraction microstructure layer and surface protective film: Located on the human eye side surface of the transparent substrate, it is formed by nanoimprinting process. Only a single layer of infrared anti-reflection protective film is deposited on the outside of the microstructure layer to avoid the failure of scintillation function caused by thick film filling the grating groove.

[0020] The detailed design of the diffraction microstructure layer is as follows: Formed on the human eye-side surface of a substrate using a nanoimprinting process, this microstructure is a blazed grating designed for 850nm infrared light, with a grating period of 2.5μm, a blaze angle of 11.2°, and a corresponding blaze depth of 1.2μm. Through groove and period optimization, it is designed to achieve a +1st order diffraction efficiency of ≥85% for 850nm infrared light, enabling directional deflection of eye-tracking illumination light; at the same time, it has a 0th order transmittance of ≥95% for 400-700nm visible light and a total non-0th order diffraction efficiency of ≤3%, avoiding interference with the displayed virtual image.

[0021] For curved substrates with refractive power, a curved nanoimprinting process is adopted, using a flexible mold that matches the curved surface of the substrate. The imprinting morphology accuracy is controlled within ±50nm, ensuring the consistency of the microstructure throughout the entire aperture.

[0022] 3.2 Multilayer Antireflective Membrane System Design To achieve high transmittance in the visible light band and high transmittance with low scattering in the infrared band, this invention employs the following combined design method: 3.2.1 Membrane system design objectives Average transmittance in the visible light band (400-700nm) ≥92%; Average transmittance in infrared bands (850±20nm, 940±20nm) ≥88%, ≥88%; Total scattering haze in the infrared band ≤1.0%; Meets the eye-tracking signal-to-noise ratio threshold ≥10dB.

[0023] 3.2.2 Material Selection High refractive index layer: TiO2 (n=2.40@550nm, extinction coefficient k<1e-4); Low refractive index layer: SiO2 (n=1.46@550nm); Substrate: Polycarbonate (n=1.58@550nm).

[0024] 3.2.3 Membrane System Optimization Methods Transmittance was calculated using the thin-film transport matrix method. The initial film structure consisted of 25 alternating TiO2 / SiO2 layers with a total thickness of approximately 2.5 μm. Numerical optimization was performed using optical thin-film design software (such as Essential Macleod or TFCalc), with the objective function being:

[0025] Among them, weight The importance of the infrared band is emphasized. The thickness of each layer is obtained after optimization; the thickness of the example section (from the substrate outwards) is as follows:

[0026] The complete 25-layer data can be stored in the design data package.

[0027] 3.2.4 Scattering Budget Model Total scattering It consists of three parts: Microstructure scattering According to scalar scattering theory, this is related to the root mean square (RMS) roughness. The RMS surface roughness of the diffracted microstructure can be controlled below 0.5 nm, corresponding to scattering... ≈0.2%.

[0028] Substrate scattering The scattering coefficient depends on the volume scattering coefficient β of the substrate material and the path length L of light propagation in the lens (here, the lens thickness; for perpendicular incident light passing through once, the path length L = 2 mm). The scattering ratio is approximately β·L. To ensure total scattering is less than or equal to 1%, a low volume scattering material must be selected. If high-purity optical polycarbonate or glass is used, the volume scattering coefficient can be as low as β1 ≤ 0.005 cm⁻¹. -1 ,but:

[0029] Film scattering Caused by the roughness of the film interface, the total scattering is approximately 0.3% as calculated using the multilayer film scattering theory, assuming that the roughness of each layer interface is RMS=0.3nm.

[0030] Therefore, the total scattering budget is:

[0031] It meets the design requirements.

[0032] 3.2.5 Tolerance Analysis The Monte Carlo method was used to subject the film thickness to 500 random perturbations (thickness deviation ±2%, normally distributed), and fluctuations in bulk scattering of the material were considered (assuming β1 is between 0.003 and 0.007 cm). -1 (Variations within a certain range) The fluctuations in infrared transmittance and scattering were statistically analyzed. The results showed that within the 95% confidence interval, the transmittance at 850nm was ≥86%, at 940nm it was ≥85%, and the total scattering haze was ≤0.8%, still meeting the requirement of ≤1.0%. If the volume scattering exceeds the tolerance, further material screening or tightening of thickness tolerance is required.

[0033] 3.2.6 Manufacturing Process Clean and dry the substrate; Diffraction microstructures were prepared on the substrate surface by nanoimprinting (using UV-cured resin, and the mold was made by electron beam lithography). Multilayer films were deposited using ion-assisted electron beam evaporation at a deposition temperature of 80℃ and a deposition rate of 0.2–0.5 nm / s. After deposition, a hydrophobic hard coating (such as Daikin OPTOOL DSX) with a thickness of about 5 nm is applied to the surface of the film.

[0034] IV. Specific Implementation Methods of Wavefront Sensors 4.1 Hardware Configuration In a preferred embodiment of the present invention, the wavefront sensor is a Shaker-Hartmann wavefront sensor, which is a dedicated calibration device for production line calibration and offline lens fitting. It is not integrated into the terminal AR glasses. The terminal device directly calls the pre-generated optical fingerprint data package to achieve compensation. The specific configuration is as follows: Microlens array size: 9×9mm, containing 60×60 sub-apertures; Sub-aperture spacing: 150μm, 60×150μm=9mm, perfectly matching the light transmission aperture; Focal length: 5mm; Sensor chip: CMOS image sensor (Basler acA1300-30gm), resolution 1280×1024, pixel size 4.8μm; Dynamic range: >1000:1; Sampling frequency: 30Hz.

[0035] The wavefront sensor is mounted on a precision electronically controlled translation stage, which can move along the optical axis to align with the exit pupil position within the eye movement range (approximately 12-15 mm from the corneal apex). The translation stage has a positioning accuracy of ±0.01 mm.

[0036] 4.2 Wavefront Detection Principle The display module projects a uniform bright field, and the light enters the wavefront sensor after passing through the waveguide sheet and prescription lens module. To address the superimposed light field after multi-pupil dilation in the diffractive waveguide, this solution employs the following adaptation measures to resolve wavefront detection distortion: During the calibration process, the control display module projects parallel light from a single field of view, sequentially covering the entire field of view, and collecting wavefront data from each field of view to avoid wavefront aliasing caused by the superposition of multiple fields of view. A field stop is set in front of the microlens array to limit the sampling field of a single sub-aperture, ensuring that the incident light in the sub-aperture is parallel light at a single angle, and eliminating the light spot diffusion caused by pupil dilation. A regional wavefront reconstruction algorithm is used to stitch and fit the wavefront data of the entire field of view to obtain the total wavefront error distribution within the entire field of view.

[0037] The core principle of wavefront detection is as follows: a microlens array divides the incident wavefront into a sub-aperture spot array, and a CMOS sensor records the position of each spot; by comparing the offset between the actual spot position and the reference position (the position corresponding to the ideal plane wave), the wavefront slope within each sub-aperture is obtained; the wavefront phase distribution is reconstructed using the mode method and fitted to the first 36 Zernike polynomials of the Fringe sequence.

[0038] 4.3 Error Vector Decomposition Method The specific implementation of wavefront sensor detection and error vector decomposition is as follows: 4.3.1 Factory Calibration (Obtaining the inherent error of the display module) Without prescription lenses, the wavefront sensor is moved to the exit pupil position. The display module projects a uniform bright field, and the wavefront sensor collects the light spot array. The offset of each sub-aperture light spot is obtained through the centroid algorithm. Calculate the wavefront slope:

[0039] Where f is the focal length of the microlens. The wavefront is fitted using Zernike polynomials (the first 36 terms) to obtain the vector of inherent error coefficients of the display module. (Unit: wavelength λ = 550 nm). Fitting equation:

[0040] Solve the coefficient vector using the least squares method. This vector is stored in the non-volatile memory of the control unit.

[0041] 4.3.2 Coupling Matrix Calibration At least 40 standard lenses were selected, covering the full range of commonly used refractive errors from -8D to +4D, including different combinations of spherical, cylindrical, and axis powers, covering most prescription scenarios used by users in their daily lives, with Zernike coefficients... Known (can be measured using an independent wavefront interferometer). Install each standard lens sequentially and measure the Zernike coefficient vector corresponding to the total wavefront error. Establish a system of equations:

[0042] Where H is the 36×36 coupling matrix to be solved. For measuring noise.

[0043] Combining all the measurement data, we obtain the overdetermined system of equations:

[0044] in The total error matrix is ​​40×36. The error matrix for a standard lens is 40×36. The coupling matrix H is solved using the least squares method.

[0045] After calibration, H is also stored in memory as a fixed parameter for subsequent error decoupling.

[0046] 4.3.3 Prescription Lens Testing After the user selects and installs the prescription lenses, the detection mode is activated. The wavefront sensor measures the total wavefront error across the entire field of view and fits the total wavefront Zernike coefficient vector. The error vector introduced by the prescription lens is calculated using a pre-calibrated coupling matrix.

[0047] in This is the Moore-Penrose pseudoinverse of the coupling matrix, used to remove measurement noise terms. We obtain... Then, it can be used for the generation of subsequent dynamic pre-compensation parameters.

[0048] 4.3.4 Detection Accuracy Control Acquisition principle: Because prescription lenses have manufacturing tolerances and are optically coupled with the display module, the aberrations introduced by the lenses can only be obtained by directly measuring the total error and decoupling them, providing the only basis for accurate compensation. Data collected: The light spot array collected by the wavefront sensor, and the first 36 coefficients of the Fringe sequence obtained by centroid calculation and Zernike fitting; Error control: By averaging 10 consecutive measurements, constant temperature control, and real-time calibration of the reference light source, measurement errors caused by environmental vibration, temperature drift, and light source fluctuations are reduced. Accuracy indicators: Without a lens, the inherent residual wavefront RMS of the display module should be less than 0.05λ; with a lens, the deviation between the calculated Zernike coefficient and the theoretical value corresponding to the nominal power of the lens should be controlled within 10%.

[0049] V. Detailed Implementation of the Infrared Detection Module 5.1 Hardware Configuration In a preferred embodiment of the present invention, the infrared detection module is configured with hardware for two application scenarios, each adapted to different detection requirements: 1. Production line / eyeglass fitting calibration scenario (full parameter testing): Utilizes independent calibration hardware, including: Infrared light source: Two infrared LEDs with peak wavelengths of 850nm and 940nm, model Osram SFH 4780S, each with a radiation power of 100mW and a half-width of 20nm. They are placed on both sides of the prescription lens module with the infrared camera to meet the coaxial optical path requirements for transmittance measurement. Infrared camera: OmniVision OV2311, resolution 1600×1200, pixel size 3μm, frame rate 60fps, with infrared bandpass filter (transmission band 800-1000nm). Control circuit: The LED driver circuit can independently control the on / off state and brightness of the two light sources. The camera is connected to the control unit via a MIPI interface.

[0050] 2. Terminal AR Glasses Scenario (Rapid Spot Check): The infrared light source and infrared camera of the eye-tracking system are reused (both the light source and camera are located on the display module side of the lens) for rapid spot checks of lens scattering intensity only. Transmittance measurement is not performed. The function switching between eye tracking and scattering spot check is achieved through time-division control.

[0051] 5.2 Infrared Detection Process Infrared detection and optical fingerprint data packet generation methods are as follows Figure 2 As shown, the details are as follows: Step 1: Installation and Alignment The prescription lens module to be tested is installed in the high-precision positioning fixture of the calibration tool to ensure that the optical axes of the infrared light source, lens and infrared camera are coaxial and coincident, and the illumination spot of the light source completely covers the camera's field of view.

[0052] Step 2: Reference Image Acquisition To establish a 100% transmittance benchmark, a reference image was acquired using the empty optical path reference method: the prescription lens module was removed from the optical path, while keeping the hardware parameters and spatial positions of the light source and camera completely unchanged. The 850nm and 940nm light sources were then illuminated, and reference spot images were recorded. and Simultaneously, acquire dark current background images when all light sources are turned off. This method requires no additional standard components and directly measures the reference light intensity of the empty optical path, resulting in higher measurement accuracy.

[0053] Step 3: Sample Image Acquisition The prescription lens module to be tested was fixed in the optical path. Keeping the light source and camera parameters unchanged, the 850nm and 940nm light sources were turned on respectively, and the light spot images after passing through the lens were recorded. and .

[0054] Step 4: Transmittance Calculation The average transmittance of the lens was calculated using the full aperture light intensity integration method:

[0055] Where ∑ represents the summation of gray values ​​over the effective area of ​​the light spot, ensuring that the calculation result reflects the average transmittance of the entire lens aperture.

[0056] Step 5: Calculation of Scattering Intensity The degree of light spot diffusion is analyzed and characterized by haze. Haze is defined as the ratio of scattered light flux at a certain angle (e.g., ±2.5°) off the principal optical axis to the total transmitted light flux. The specific calculation steps are as follows: 1. Calculate the position of the centroid of the light spot. ; 2. Determine the core region: Using the centroid as the center, calculate the radius r of the circular region corresponding to a scattering angle of ±2.5° (when the camera is 50mm away from the lens, r = 50 × tan(2.5°) ≈ 2.18mm, converted to the corresponding number of pixels), and calculate the luminous flux within the core region. ; Calculate the total luminous flux of the entire light spot. (Sum of the entire image after removing the dark background); Calculate haze:

[0057] To ensure measurement accuracy, it is recommended to perform multiple acquisitions (e.g., 10 times) for each light source, take the average, and subtract the dark current background.

[0058] 5.3 Optical fingerprint data packet format Organize the data using JSON format, as shown in the example below: json { "lens_id": "LENS-20260309-001", "design_version": "v2.1", "manufacturing_batch": "BATCH-2405", "timestamp": "2026-03-09T10:30:00Z", "prescription_errors": { "z4": -2.85, "z5": 0.12, "z6": 0.03, "z7": -0.05, "z8": 0.02, "z9": 0.01, "z10": 0.00, "z11": 0.00 }, "ir_performance": { "T850": 0.89, "T940": 0.87, "haze_850": 0.008, "haze_940": 0.007 }, "signature": "a1b2c3d4e5f6..." } The data packet includes a tamper-proof verification field to ensure the integrity and authenticity of the data.

[0059] After the data packet is generated, it is stored in the memory of the control unit and associated with the design data packet (containing membrane parameters, scattering budget, optimization objectives, etc.) in the design phase through lens_id, supporting data traceability.

[0060] VI. Specific Implementation Methods of Control Unit and Dynamic Pre-compensation Algorithm 6.1 Control Unit Hardware In a preferred embodiment of the present invention, the control unit adopts an embedded processor Qualcomm Snapdragon XR2 platform, integrating: CPU: Kyro 585 octa-core, up to 2.84GHz; GPU: Adreno 650, supporting graphics rendering and general computing; DSP: Hexagon 698, used for sensor data processing; ISP: Dual 14-bit, supporting processing up to 120 million pixels per second.

[0061] The control unit controls the wavefront sensor and infrared detection module via the I²C interface, reads and writes to the memory via the SPI interface, and connects to the display module's drive circuit via the MIPI interface. The non-volatile memory uses UFS 3.0 with a capacity of 128GB, used to store system programs, calibration data, optical fingerprint data packets, etc.

[0062] 6.2 Dynamic Pre-compensation Algorithm Introducing an error vector based on prescription lenses The present invention employs two compensation methods: pixel displacement matrix (adapted to amplitude-type displays) or phase modulation amount (adapted to phase-type spatial light modulators).

[0063] 6.2.1 Distortion Sensitivity Matrix Calibration Because the geometric distortion introduced by prescription lenses is spatially variable, the offset at different pixel positions... Not only with the Zernike coefficient It is also related to the ideal coordinates of the pixel. Therefore, a mapping relationship dependent on pixel position needs to be established.

[0064] Step 1: Mesh Generation The ideal plane for displaying the image is divided into a uniform grid, for example, 32×32 grid points, with each grid point having coordinates of... The grid density can be adjusted according to the complexity of the distortion; for example, it can be increased to 64×64 for high-distortion lenses.

[0065] Step 2: Local Sensitivity Matrix Calibration For each grid point Through ray tracing simulation or field calibration, a model based on the Zernike coefficient can be established. A linear model of the offset to that point. The specific method is as follows: Simulation Calibration: A complete optical path model including the display module and prescription lenses is established in optical design software (such as Zemax or Code V). The wavefront aberrations introduced by the lenses are expressed using Zernike polynomials. By changing the Zernike coefficients (e.g., introducing Zernike terms of various orders in unit quantities sequentially), the ideal mesh points are simulated and calculated. Corresponding actual image plane position , obtain the offset .

[0066] For each grid point, multiple sets of Zernike coefficients and corresponding offset data are collected (e.g., by simulating different prescriptions by changing lens power), and a 2×36 local sensitivity matrix is ​​obtained by fitting using the least squares method. ,satisfy:

[0067] in It is a 36-dimensional Zernike coefficient vector.

[0068] Actual calibration: If a precise simulation model is unavailable, actual measurement can be used. Project the grid onto the display module, and use an external high-resolution camera to capture the virtual image of the prescription lens under test. Extract the actual position of each grid point. For multiple standard lenses of different prescriptions (known...) By performing measurements, the values ​​for each grid point can also be fitted. .

[0069] Step 3: Store the local sensitivity matrix The 32×32 obtained from the calibration The matrices (each containing 2 × 36 = 72 floating-point numbers) are stored in the control unit's memory. The data size is approximately 32 × 32 × 72 × 4 bytes ≈ 294KB, which is acceptable.

[0070] 6.2.2 Pixel displacement matrix generation In practical compensation, for any pixel coordinate (u, v), its corresponding offset needs to be calculated. Since pixel positions are continuous, discrete grid points cannot be directly used. The sensitivity matrix of the pixel needs to be obtained through interpolation. Then, the offset is calculated. The specific steps are as follows: Step 1: Determine the corresponding mesh cell Given pixel coordinates (u, v), find the grid cell containing it, i.e., find i, j such that .

[0071] Step 2: Calculate the local sensitivity matrix using bilinear interpolation Using the local sensitivity matrix at the four vertices of this mesh cell The value at (u,v) is obtained through bilinear interpolation. :

[0072] in .

[0073] Step 3: Calculate pixel offset

[0074] in This is the Zernike coefficient vector obtained from the actual measurement of the current lens.

[0075] Step 4: Generate compensated coordinates The compensated pixel positions are: (u',v')=(u,v)+(Δu,Δv) Since Δu and Δv are usually non-integers, image resampling techniques (such as bilinear interpolation) are needed to obtain the pixel value at that location from the original image.

[0076] Step 5: Build a lookup table (LUT) to speed up the process. To improve real-time performance, the compensated coordinates (u', v') of all pixels can be pre-calculated, generating a pixel displacement lookup table. For a 1920×1080 resolution, storing two float values ​​(Δu, Δv) for each pixel requires approximately 16MB (1920×1080×8), which can be implemented in the display driver chip. During display, the input image is directly resampled and output based on the LUT.

[0077] 6.2.3 Phase Modulation Compensation If the display module uses a spatial light modulator, a compensated phase map can be directly generated. The Zernike coefficients are then converted into a phase distribution.

[0078] in The expression is a normalized Zernike polynomial (in polar coordinates), and (x,y) are the coordinates of the pupil plane.

[0079] The compensated phase map is loaded onto the spatial light modulator to achieve phase modulation of the incident light wave, thereby offsetting the wavefront error introduced by the lens.

[0080] VII. Plug and play lens replacement logic like Figure 3 As shown, when a user changes their prescription lenses, the system automatically compensates through the following steps: Step 1: Lens Recognition An RFID tag or a printed QR code is embedded in the edge of the lens, and the system reads the lens's unique ID through an RFID reader or camera.

[0081] Step 2: Data Packet Loading The system retrieves the corresponding optical fingerprint data packet from local storage or a cloud database based on the ID. If no corresponding data is found locally, it retrieves and downloads it from the cloud database via wireless network. If no valid data packet is found in either location, the user is prompted to visit an offline optometry clinic to complete the calibration test and generate the corresponding data packet.

[0082] Step 3: Update compensation parameters Parsing the prescription lens in the data packet introduces an error vector. The pre-calibrated distortion sensitivity matrix is ​​called to generate a new pixel displacement matrix or phase modulation map, which is then loaded into the driver chip of the display module to complete the update of the pre-distortion compensation parameters.

[0083] Step 4: Infrared Verification Quickly verify that infrared transmittance and scattering still meet the thresholds (optional; can be skipped if the infrared data in the data packet is sufficiently recent and the lens is clean). If performance degradation is detected (e.g., lens contamination), issue a cleaning prompt.

[0084] Step 5: Complete The system switches to the new compensation parameters and projects the virtual image after pre-distortion processing normally, achieving plug-and-play personalized compensation.

[0085] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. 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.

Claims

1. An augmented reality prescription lens module calibration and compensation system based on error vector decomposition, characterized in that, include: Display module, used to project virtual images; A prescription lens module is detachably mounted on the light-emitting side of the display module. The prescription lens module includes a transparent substrate, a diffraction microstructure layer formed on the substrate, and a multilayer antireflective film system covering the diffraction microstructure layer. A wavefront sensor, positioned at the exit pupil position within the eye movement range, is used to detect the total wavefront error after passing through the display module and the prescription lens module; An infrared detection module, comprising an infrared light source and an infrared camera, is used to detect the transmittance and scattering intensity of the prescription lens module in the infrared band. The control unit is communicatively connected to the display module, wavefront sensor, and infrared detection module. The control unit is configured as follows: Based on the total wavefront error, the Zernike coefficient vector of the total wavefront error is obtained by fitting a Zernike polynomial. The Zernike polynomial adopts the internationally used Fringe sequence, and the number of fitting terms is no less than the first 36 terms. The Zernike coefficient vector of the total wavefront error is decoupled by a pre-calibrated coupling matrix to decompose the display module's inherent error vector and the prescription lens-introduced error vector. Dynamic pre-compensation parameters are generated based on the error vector introduced by the prescription lens; The dynamic pre-compensation parameters are loaded into the display module to perform pre-distortion processing on the projected virtual image; An optical fingerprint data packet is generated and stored, the data packet containing at least: a unique identifier for the lens, an error vector introduced by the prescription lens, and infrared transmittance and scattering intensity data measured by the infrared detection module.

2. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The coupling matrix is ​​obtained through the following pre-calibration steps: At least 40 standard lenses covering the full range of commonly used refractive errors were selected, and the Zernike coefficient vector of each standard lens was independently calibrated using a high-precision wavefront interferometer. Each standard lens is installed sequentially on the light-emitting side of the display module. The Zernike coefficient vector corresponding to the total wavefront error is measured by the wavefront sensor, and an overdetermined system of equations is constructed. A 36×36 coupling matrix is ​​obtained by solving the least squares method, which is used to achieve stable decoupling between the inherent error vector of the display module and the error vector introduced by the prescription lens.

3. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The multilayer antireflective coating system has an average transmittance of ≥92% in the visible light band, an average transmittance of ≥88% in the 850nm and 940nm infrared bands, and the total scattering haze value of the prescription lens module in the infrared band is ≤1.0%.

4. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The dynamic pre-compensation parameter is a pixel displacement matrix or a phase modulation amount; the pixel displacement matrix is ​​generated based on the mapping relationship between the error vector introduced by the prescription lens and the geometric distortion, and is used to adjust the display position pixel by pixel at the image source level; the phase modulation amount is used to control the spatial light modulator to load the compensation phase.

5. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The optical fingerprint data package also includes a design version number, manufacturing batch information, and fields associated with the lens design data package.

6. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The infrared detection module reuses the infrared camera of the eye-tracking system to automatically measure the transmittance and scattering intensity of infrared light by the prescription lens module during the lens fitting process or use, and compares the measurement results with a preset threshold. If the result is lower than the threshold, an alarm is issued or a calibration process is triggered.

7. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The control unit is further configured to: when it detects that the prescription lens module has been replaced, automatically read the optical fingerprint data packet of the new lens, and update the dynamic pre-compensation parameters based on the error vector introduced by the prescription lens of the new lens, so as to realize plug-and-play personalized compensation.

8. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The display module includes a Micro OLED display screen and a projection optical system, wherein the projection optical system adopts a multi-piece plastic aspherical lens design.

9. The augmented reality prescription lens module calibration and compensation system based on error vector decomposition according to claim 1, characterized in that: The diffraction microstructure layer of the prescription lens module is a blazed grating designed for 850nm infrared light, with a grating period of 2.5μm, a blaze angle of 11.2°, and a blaze depth of 1.2μm.

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