Image projector multi-dimensional light path self-adaptive adjustment control method and image projector multi-dimensional light path self-adaptive adjustment control system

Abstract

The invention relates to the technical field of image projectors, in particular to a multi-dimensional light path self-adaptive adjustment control method and system for an image projector, and the method comprises the steps: obtaining multi-path real-time light path parameters, carrying out aberration noise covariance estimation to generate an aberration matrix, and generating a light path crosstalk matrix through crosstalk analysis; predicting light path change based on the aberration matrix and the crosstalk matrix to construct a multi-dimensional light path simulation model; carrying out light valve duty ratio calculation on the model to obtain a basic driving sequence, and generating an anti-crosstalk driving sequence through dead zone compensation; performing phase quantization and peak shifting distribution on the anti-crosstalk driving sequence to obtain a peak shifting control sequence; and performing temperature and ambient light compensation on the off-peak control sequence, generating a temperature compensation driving sequence, performing multi-light path cooperative processing to obtain a multi-dimensional control sequence, collecting feedback light intensity and aberration data, optimizing a simulation model, and realizing multi-dimensional light path adaptive adjustment. The problems that a traditional manual adjusting method is slow in response and poor in environmental adaptability are solved.

Description

Technical Field

[0001] This invention relates to the field of image projector technology, and in particular to a multi-dimensional optical path adaptive adjustment control method and system for image projectors. Background Technology

[0002] As a core display device integrating optical design, electronic control and precision mechanics, the optical path adjustment precision of an image projector directly determines the stability of the projected image quality and the imaging quality. It has key application value in complex scenarios such as high-end display, virtual reality, and laser projection.

[0003] However, in actual operation, multi-path optical systems are susceptible to interference from multiple factors, leading to a decline in projection performance. Specifically, the aberrations of the optical elements themselves can disrupt the collimation and focusing accuracy of the beam, causing blurred edges or color shifts in the image; changes in ambient temperature can cause thermal expansion and contraction of the optical elements and refractive index drift, resulting in optical path deviation and disordered energy distribution; and the response delay of the light valve can cause timing matching errors between different optical paths, exacerbating optical path crosstalk problems. Traditional projector optical path adjustment methods often rely on simple manual control, which results in slow response to environmental changes and makes real-time adjustment difficult.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main objective of this invention is to provide a multi-dimensional adaptive optical path adjustment control method and system for an image projector, aiming to solve the technical problem that traditional projector optical path adjustment methods often rely on simple manual control, which leads to slow response speed to environmental changes and difficulty in achieving real-time adjustment.

[0006] To achieve the above objectives, the present invention provides a multi-dimensional optical path adaptive adjustment control method for an image projector, the method comprising: The parameters of multiple real-time optical paths in the image projector are obtained, the aberration noise covariance of the parameters is estimated to obtain the aberration matrix, and the crosstalk of optical components is analyzed to generate the crosstalk matrix. Based on the aberration matrix and the optical path crosstalk matrix, the optical path parameters in real time are predicted to generate predicted optical path parameters. Based on the predicted optical path parameters, the optical path model is simulated to obtain a multi-dimensional optical path simulation model of the image projector. The optical valve duty cycle of the multi-dimensional optical path simulation model is calculated to obtain the basic driving sequence. The optical valve dead zone compensation process is then applied to the basic driving sequence to generate an anti-crosstalk driving sequence. The anti-crosstalk driving sequence is phase quantized to obtain a quantized phase sequence. Based on the quantized phase sequence, the anti-crosstalk driving sequence is phase staggered to obtain a staggered control sequence. Multi-path temperature compensation and ambient light compensation are performed on the peak-shifting control sequence to obtain a temperature-compensated driving sequence. Multi-optical path collaborative processing is then performed on the temperature-compensated driving sequence to generate a multi-dimensional control sequence. The optical path adjustment control of the image projector is performed according to the multi-dimensional control sequence. At the same time, feedback light intensity and aberration data are collected. Based on the feedback light intensity and aberration data, the multi-dimensional optical path simulation model is optimized to perform multi-dimensional optical path adaptive adjustment of the image projector.

[0007] Optionally, the step of acquiring multiple real-time optical path parameters in the image projector, estimating the aberration noise covariance of the multiple real-time optical path parameters to obtain an aberration matrix, and performing optical element crosstalk analysis on the multiple real-time optical path parameters to generate an optical path crosstalk matrix includes: The real-time light intensity, aberration, and optical path offset parameters of the light valve unit, lens group, and prism in the image projector are obtained to form multiple real-time optical path parameters; Spatial filtering is performed on the multi-channel real-time optical path parameters to remove high-frequency noise and obtain the filtered optical path parameters. Optical state space mapping is performed on the filtered optical path parameters to construct an optical path state matrix that includes light intensity distribution and aberration coefficients; The noise covariance of the optical path state matrix is ​​estimated, and the noise correlation of each optical path channel is extracted to obtain the aberration matrix. A network interference topology is constructed based on the optical element layout of the image projector. Optical element crosstalk analysis is performed on the multi-path real-time optical parameters. The optical element crosstalk includes light leakage between optical valves and lens aberration coupling interference, generating an optical path crosstalk matrix.

[0008] Optionally, the step of predicting optical path changes in real-time optical path parameters based on the aberration matrix and the optical path crosstalk matrix to generate predicted optical path parameters, and performing optical path model simulation based on the predicted optical path parameters to obtain a multi-dimensional optical path simulation model of the image projector, includes: The crosstalk correlation matrix is ​​obtained by weighting the aberration matrix with channel correlation using the optical path crosstalk matrix. Based on the crosstalk correlation matrix, spectral aberration transformation is performed on the real-time optical path parameters to generate an optical path spectrum matrix containing wavelength, light intensity, and aberration coefficients. The optical path spectrum matrix is ​​temporally extrapolated using a dynamic prediction model to predict the light intensity distribution and aberration variation trend of each optical path channel, thereby obtaining the predicted optical path parameters. Based on the preset optical path geometry parameters and optical valve response characteristics of the image projector, the predicted optical path parameters are mapped to execution parameters, which include optical valve driving voltage and lens displacement. Based on the execution parameters, the topology of the light valve array, lens group, and prism in the image projector is reconstructed to build a multi-dimensional optical path simulation model containing dynamic optical elements.

[0009] Optionally, the step of calculating the optical valve duty cycle of the multi-dimensional optical path simulation model to obtain a basic driving sequence, and performing optical valve dead-zone compensation processing on the basic driving sequence to generate an anti-crosstalk driving sequence, includes: The multi-dimensional optical path simulation model is subjected to channel brightness equalization processing, and the optical path difference is eliminated by gamma correction algorithm to obtain the equalized light intensity matrix; Based on the equalized light intensity matrix and the light valve response curve, the linear relationship between the light valve opening / closing time and the light intensity is deduced, and the light valve driving equalization matrix is ​​generated. The duty cycle of the light valve drive equalization matrix is ​​calculated in multiple dimensions, and a basic drive sequence is generated in combination with the frame rate requirements of the image projector. The basic driving sequence is subjected to optical valve edge switching conflict detection to identify synchronous switching interference points of adjacent optical valve units and generate timing scheduling parameters. Based on the aforementioned timing scheduling parameters, dead time compensation is performed on the basic drive sequence, non-overlapping drive intervals are increased, and anti-crosstalk drive sequences are generated.

[0010] Optionally, the step of performing phase quantization on the anti-crosstalk driving sequence to obtain a quantized phase sequence, and performing phase staggering allocation on the anti-crosstalk driving sequence based on the quantized phase sequence to obtain a staggering control sequence, includes: The anti-crosstalk driving sequence is divided into phase periods, and phase quantization is performed based on the light valve driving period of the image projector to obtain a quantized phase sequence. The channel distribution density of each phase interval in the quantized phase sequence is statistically analyzed to generate a quantized phase sequence distribution histogram. Based on the quantized phase sequence distribution histogram, the power of the optical valve channel is grouped to construct a phase configuration reference system with balanced heat dissipation; The anti-crosstalk drive sequence is rearranged across channels according to the phase configuration reference system, so that the drive phase of the high-power channel is dispersed in different period intervals, thereby generating a peak-shifting control sequence.

[0011] Optionally, the step of performing multi-path temperature compensation and ambient light compensation on the peak-shifting control sequence to obtain a temperature-compensated driving sequence, and performing multi-optical path collaborative processing on the temperature-compensated driving sequence to generate a multi-dimensional control sequence, includes: Based on the thermal expansion coefficients of the light valves and lenses in the image projector and real-time temperature sensor data, the peak shaving control sequence is mapped to optical path offset parameters under the influence of temperature. Temperature coefficient compensation is performed on the driving parameters of each channel to correct the response delay of the light valve and the change in lens focal length caused by thermal deformation, and the temperature-compensated driving sequence of each channel is obtained. The ambient light data collected by the ambient light sensor of the image projector is used to dynamically adjust the gain of the temperature compensation drive sequence to compensate for the influence of ambient light on the projection brightness. Multi-optical-path collaborative optimization is performed on the compensated temperature-compensated drive sequence, and the inter-channel light intensity equalization correction coefficient is calculated. Based on the correction coefficient, the light flux output model of each optical path in the image projector is reconstructed to generate a light efficiency compensation sequence. The light effect compensation sequence is subjected to time-domain synchronization processing to ensure alignment with the frame rate and image processing timing of the image projector, thereby generating a multi-dimensional control sequence.

[0012] Optionally, the step of performing optical path adjustment control of the image projector according to the multi-dimensional control sequence, simultaneously collecting feedback light intensity and aberration data, and performing feedback optimization of the multi-dimensional optical path simulation model based on the feedback light intensity and aberration data to execute multi-dimensional adaptive optical path adjustment of the image projector includes: The multidimensional control sequence is converted into hardware-executable signals for the image projector, including light valve drive voltage signals and lens servo motor control signals. The hardware can execute signals to drive the light valve and lens group of the image projector to operate, and simultaneously collect the light intensity distribution and aberration residual data at the center and edge of the projected image to form feedback light intensity and aberration data. The feedback light intensity and aberration data are compared with the theoretical output of the multi-dimensional optical path simulation model to calculate the comparison parameters, which include light intensity uniformity deviation and aberration residual value. Based on the comparison parameters, the light valve response parameters and lens deformation coefficient in the multi-dimensional optical path simulation model are adaptively corrected, the parameters of the multi-dimensional optical path simulation model are updated, a closed-loop adjustment is formed, and the multi-dimensional optical path adaptive adjustment of the image projector is realized.

[0013] Furthermore, to achieve the above objectives, the present invention also provides a multi-dimensional optical path adaptive adjustment control system for an image projector, the multi-dimensional optical path adaptive adjustment control system for an image projector comprising: The parameter parsing module is used to obtain multiple real-time optical path parameters in the image projector, perform aberration noise covariance estimation on the multiple real-time optical path parameters to obtain an aberration matrix, and perform optical element crosstalk analysis on the multiple real-time optical path parameters to generate an optical path crosstalk matrix. The prediction modeling module is used to predict optical path changes in real-time optical path parameters based on the aberration matrix and the optical path crosstalk matrix, generate predicted optical path parameters, and perform optical path model simulation based on the predicted optical path parameters to obtain a multi-dimensional optical path simulation model of the image projector. The drive compensation module is used to calculate the optical valve duty cycle of the multi-dimensional optical path simulation model to obtain the basic drive sequence, and to perform optical valve dead zone compensation processing on the basic drive sequence to generate an anti-crosstalk drive sequence. The phase allocation module is used to perform phase quantization on the anti-crosstalk driving sequence to obtain a quantized phase sequence, and to perform phase staggering allocation on the anti-crosstalk driving sequence based on the quantized phase sequence to obtain a staggering control sequence. The temperature compensation coordination module is used to perform multi-path temperature compensation and ambient light compensation on the peak shaving control sequence to obtain a temperature compensation driving sequence, and to perform multi-optical path coordination processing on the temperature compensation driving sequence to generate a multi-dimensional control sequence. The feedback optimization module is used to adjust and control the optical path of the image projector according to the multi-dimensional control sequence, and simultaneously collect feedback light intensity and aberration data. Based on the feedback light intensity and aberration data, the module performs feedback optimization on the multi-dimensional optical path simulation model to execute multi-dimensional optical path adaptive adjustment of the image projector.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a multi-dimensional optical path adaptive adjustment control device for an image projector, the device comprising: a memory, a processor, and an image projector multi-dimensional optical path adaptive adjustment control program stored in the memory and executable on the processor, the image projector multi-dimensional optical path adaptive adjustment control program being configured to implement the steps of the image projector multi-dimensional optical path adaptive adjustment control method as described above.

[0015] In addition, to achieve the above objectives, the present invention also provides a storage medium storing a multi-dimensional optical path adaptive adjustment control program for an image projector. When the multi-dimensional optical path adaptive adjustment control program for an image projector is executed by a processor, it implements the steps of the multi-dimensional optical path adaptive adjustment control method for an image projector as described above.

[0016] This invention provides a multi-dimensional adaptive optical path adjustment and control method for an image projector. This method achieves quantitative modeling of nonlinear interferences such as optical element aberrations and optical path crosstalk by estimating the aberration noise covariance and analyzing the crosstalk matrix of multiple real-time optical path parameters, breaking through the traditional method's reliance on empirical parameters for coarse adjustment. Combining optical path change prediction with a multi-dimensional optical path simulation model, the method can predict the impact of dynamic factors such as environmental temperature changes and optical valve delay on the optical path in advance, enabling the system to proactively compensate for problems such as spherical aberration and color shift, significantly improving the optical path stability in complex scenarios. Through optical valve dead zone compensation and phase shifting allocation, the method effectively solves the timing mismatch problem caused by optical valve response delay in traditional methods. Specifically, dead-zone compensation eliminates the nonlinear blind zone of the light valve drive signal, avoiding overlapping interference from multiple optical path drive signals. The staggered control sequence, through phase quantization and dynamic timing allocation, ensures that each optical path drive operates at off-peak times in the time dimension, fundamentally suppressing color aliasing and brightness unevenness caused by crosstalk, and improving the color purity and energy distribution uniformity of the projected image. To address environmental temperature variations and light intensity fluctuations, the solution introduces multi-path temperature compensation and ambient light compensation mechanisms to correct optical path offsets and refractive index drift caused by thermal deformation of optical components in real time, while adaptively adjusting the light valve drive energy to match changes in ambient light. Compared to traditional manual adjustment or fixed threshold compensation, this dynamic compensation mechanism can complete parameter iteration within millisecond timescales, significantly reducing the impact of extreme environments such as temperature gradients and strong outdoor light on projected image quality, ensuring the stability of image brightness, contrast, and geometric accuracy. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of the image projector multi-dimensional optical path adaptive adjustment and control device in the hardware operating environment involved in the embodiments of the present invention; Figure 2 This is a flowchart illustrating the first embodiment of the multi-dimensional optical path adaptive adjustment and control method for an image projector according to the present invention. Figure 3 This is a structural block diagram of the first embodiment of the multi-dimensional optical path adaptive adjustment and control system for the image projector of the present invention.

[0018] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0019] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0020] Reference Figure 1 , Figure 1 This is a schematic diagram of the structure of a multi-dimensional optical path adaptive adjustment control device for an image projector in the hardware operating environment of an embodiment of the present invention.

[0021] like Figure 1 As shown, the multi-dimensional optical path adaptive adjustment control device for the image projector may include: a processor 1001, such as a central processing unit (CPU), a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. The communication bus 1002 is used to enable communication between these components. The user interface 1003 may include a display screen, and optionally, it may also include a standard wired interface or a wireless interface. In this invention, the wired interface of the user interface 1003 may be a USB interface. The network interface 1004 may optionally include a standard wired interface or a wireless interface (such as a Wi-Fi interface). The memory 1005 may be a high-speed random access memory (RAM) or a non-volatile memory (NVM), such as a disk storage device. The memory 1005 may also optionally be a storage device independent of the aforementioned processor 1001.

[0022] Those skilled in the art will understand that Figure 1 The structure shown does not constitute a limitation on the multi-dimensional optical path adaptive adjustment control device for image projectors, and may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0023] like Figure 1 As shown, the memory 1005, which serves as a computer storage medium, may include an operating system, a network communication module, a user interface module, and a multi-dimensional optical path adaptive adjustment control program for an image projector.

[0024] exist Figure 1 In the multi-dimensional optical path adaptive adjustment control device for the image projector shown, the network interface 1004 is mainly used to connect to the backend server and communicate data with the backend server; the user interface 1003 is mainly used to connect to peripherals; the multi-dimensional optical path adaptive adjustment control device for the image projector calls the multi-dimensional optical path adaptive adjustment control program for the image projector stored in the memory 1005 through the processor 1001, and executes the multi-dimensional optical path adaptive adjustment control method for the image projector provided in this embodiment of the invention.

[0025] Based on the above hardware structure, an embodiment of the multi-dimensional optical path adaptive adjustment and control method for image projectors of the present invention is proposed.

[0026] Reference Figure 2 , Figure 2This is a flowchart illustrating the first embodiment of the multi-dimensional optical path adaptive adjustment and control method for an image projector according to the present invention. The first embodiment of the multi-dimensional optical path adaptive adjustment and control method for an image projector according to the present invention is presented.

[0027] In one embodiment, a multi-dimensional optical path adaptive adjustment control method for an image projector is provided, the method comprising: Step S100: Obtain the multi-channel real-time optical path parameters in the image projector, perform aberration noise covariance estimation on the multi-channel real-time optical path parameters to obtain the aberration matrix, and perform optical element crosstalk analysis on the multi-channel real-time optical path parameters to generate the optical path crosstalk matrix.

[0028] The aberration matrix can be a mathematical model describing the distribution characteristics and interrelationships of aberration noise among different optical path parameters. It can be obtained by calculating the covariance of sample data using statistical methods. For example, the construction of the aberration matrix can include algorithms such as least squares or Kalman filtering. The optical path crosstalk matrix can be a matrix model quantifying the intensity of energy leakage or signal interference between different optical paths. It can be obtained by detecting the response changes of other optical paths after a known signal is applied to a specific optical path. For example, optical path crosstalk analysis can include measuring the impact of changes in beam intensity distribution or optical element position offset on adjacent optical paths. Real-time optical path parameters are acquired and aberration and crosstalk matrices are generated. For example, parameters such as optical element position offset and optical valve drive signal response characteristics can be collected in real time by sensors, and multi-dimensional parameter correlation analysis can be performed in combination with environmental sensor data to construct an aberration and crosstalk model reflecting the dynamic characteristics of the system.

[0029] Step S200: Based on the aberration matrix and the optical path crosstalk matrix, predict the optical path changes of the real-time optical path parameters, generate predicted optical path parameters, and perform optical path model simulation based on the predicted optical path parameters to obtain a multi-dimensional optical path simulation model of the image projector.

[0030] The predicted optical path parameters can be the predicted trends of optical path parameter changes at future times, which can be calculated using a time series prediction model or a physical driving model. For example, time series prediction can include an ARIMA model predicting refractive index drift caused by temperature changes. The multi-dimensional optical path simulation model can be a dynamic simulation system integrating optical propagation, mechanical deformation, and electronic control coupling effects. It can be generated using Monte Carlo simulation or ray tracing algorithms. For example, the output of the simulation model can include parameters such as optical path offset, energy distribution changes, and aberration evolution. Based on the aberration matrix and optical path crosstalk matrix, optical path change prediction and simulation model construction are performed. For example, environmental sensor data can be input into the simulation model, and the influence of thermal deformation on the position of optical components can be simulated through finite element analysis. The simulation results, including multi-dimensional dynamic characteristics, are output, thus providing predictive references for subsequent driving sequence design.

[0031] Step S300: Calculate the duty cycle of the optical valve in the multi-dimensional optical path simulation model to obtain the basic driving sequence. Perform optical valve dead zone compensation processing on the basic driving sequence to generate an anti-crosstalk driving sequence.

[0032] The basic drive sequence can be a pulse width modulation (PWM) control signal sequence generated based on the predicted requirements of the simulation model. It can be calculated by adjusting the ratio of high to low levels of the drive signal. For example, the duty cycle calculation can be based on the opening and closing time requirements of the optical valve and the system response time constraints. The anti-crosstalk drive sequence can be a compensated drive sequence that eliminates the invalid interval of the drive signal. This can be achieved through adaptive threshold adjustment or voltage offset correction. For example, dead-time compensation processing can include superimposing a compensation voltage or adjusting the rise / fall slope of the pulse. Duty cycle calculation and dead-time compensation processing are performed on the optical valve drive sequence. For example, compensation parameters can be determined through iterative optimization algorithms based on optical valve response characteristic test data to ensure that the drive signal responds linearly across the entire voltage range, thereby reducing uneven energy distribution in the optical path caused by drive distortion.

[0033] Step S400: Phase quantization is performed on the anti-crosstalk driving sequence to obtain a quantized phase sequence. Based on the quantized phase sequence, phase staggering is performed on the anti-crosstalk driving sequence to obtain a staggering control sequence.

[0034] The quantized phase sequence can be an identifier sequence that divides the drive cycle into discrete time units. This can be obtained by combining the optical valve response time and the system clock precision. For example, phase quantization can divide the drive cycle into multiple time slots and assign phase identifiers. The peak-shifting control sequence can be a timing control sequence that adjusts the trigger times of the drive signals for each optical path. This can be implemented using a dynamic timing algorithm. For example, peak-shifting allocation can employ polling scheduling or priority allocation strategies to ensure that the interval between adjacent optical path drive signals meets the dead-zone recovery time requirement. Phase quantization and peak-shifting control of the drive sequence can, for example, be based on the optical valve mechanical response delay and the system synchronization clock. An optimization algorithm can be used to assign phase identifiers and generate a control sequence with staggered trigger times, thereby suppressing optical interference and energy superposition effects caused by the overlap of multiple drive signals.

[0035] Step S500: Perform multi-path temperature compensation and ambient light compensation on the peak-shifting control sequence to obtain the temperature-compensated driving sequence. Perform multi-path collaborative processing on the temperature-compensated driving sequence to generate a multi-dimensional control sequence.

[0036] The temperature-compensated drive sequence can be a drive sequence including temperature deformation compensation parameters, generated through PID closed-loop control or neural network prediction algorithms. For example, temperature compensation can be combined with distributed temperature sensor data to calculate the optical element position offset and adjust the drive parameters. The multidimensional control sequence can be a set of coordinated drive parameters integrating temperature compensation and ambient light compensation, generated through weighted optimization or constraint programming algorithms. For example, collaborative processing can include weighted fusion of drive parameters for each optical path to meet synchronization and consistency requirements. Temperature and ambient light compensation and collaborative processing are performed. For example, an ambient light sensor can detect the illumination intensity of the projection area, an adaptive gain adjustment algorithm can be used to adjust the transmittance or duty cycle of the light valve, and a thermodynamic model can be used to predict the impact of temperature changes on the optical path, ultimately generating a multidimensional control sequence that coordinates the parameters of each optical path.

[0037] Step S600: The optical path adjustment control of the image projector is performed according to the multi-dimensional control sequence. At the same time, feedback light intensity and aberration data are collected. The multi-dimensional optical path simulation model is optimized based on the feedback light intensity and aberration data to perform multi-dimensional optical path adaptive adjustment of the image projector.

[0038] The multidimensional control sequence can be a comprehensive control command including mechanical position adjustment, voltage regulation, or optical element deflection parameters. It can be implemented through a motor drive or voltage regulation device. For example, the control sequence may include adjusting the reflector angle or the light valve drive voltage. Feedback optimization can be an online parameter correction process based on Kalman filtering or Bayesian updates. This can be achieved through data from photoelectric sensors and wavefront sensors. For example, comparing feedback data with simulation prediction results can be used to adjust the weights of the aberration covariance matrix or update the crosstalk matrix coefficients. Optical path adjustment and feedback optimization are performed based on the multidimensional control sequence. For example, real-time acquisition of feedback light intensity distribution and aberration change data can be used to correct simulation model parameters through iterative optimization algorithms, thereby continuously improving the optical path adjustment accuracy and system robustness.

[0039] This embodiment provides a multi-dimensional adaptive optical path adjustment and control method for image projectors. It quantifies optical interference characteristics by real-time acquisition of multiple optical path parameters and constructing aberration and crosstalk matrices. A predictive drive sequence is generated by combining time series prediction and a multi-dimensional simulation model. Nonlinear effects of the drive signal are eliminated through duty cycle calculation and dead-zone compensation. Phase quantization and peak offset allocation suppress multi-path drive timing interference. Millisecond-level parameter adaptive adjustment is achieved based on temperature and ambient light compensation. Closed-loop feedback optimization continuously corrects model parameters, significantly improving the optical path stability and dynamic response accuracy of image projectors in complex environments. This method effectively solves the problems of response lag and coarse adjustment in traditional adjustment methods through the synergistic effect of multi-dimensional modeling, predictive simulation, and real-time feedback control. It achieves stable output with high resolution, color fidelity, and geometric accuracy, making it suitable for applications with stringent requirements for optical path control precision, such as high-end displays and virtual reality.

[0040] In one embodiment, multiple real-time optical path parameters in the image projector are acquired, aberration noise covariance is estimated on the multiple real-time optical path parameters to obtain an aberration matrix, and optical element crosstalk analysis is performed on the multiple real-time optical path parameters to generate an optical path crosstalk matrix, including: The real-time light intensity, aberration, and optical path offset parameters of the light valve unit, lens group, and prism in the image projector are obtained to form multiple real-time optical path parameters; Spatial filtering is performed on the multi-channel real-time optical path parameters to remove high-frequency noise and obtain the filtered optical path parameters; Optical state space mapping is performed on the filtered optical path parameters to construct an optical path state matrix that includes light intensity distribution and aberration coefficients; The noise covariance of the optical path state matrix is ​​estimated, and the noise correlation of each optical path channel is extracted to obtain the aberration matrix. Based on the optical component layout of the image projector, a network interference topology is constructed. Crosstalk analysis of optical components is performed on the parameters of multiple real-time optical paths. The crosstalk of optical components includes light leakage between light valves and lens aberration coupling interference, and an optical path crosstalk matrix is ​​generated.

[0041] The real-time light intensity, aberration, and optical path offset parameters of the light valve unit, lens group, and prism can be a set of multi-dimensional physical quantities describing the dynamic characteristics of optical elements. These parameters can be obtained in real-time through a distributed sensor array, such as a photodiode or displacement sensor. For example, the real-time light intensity parameters can include the spatial distribution of light flux at different wavelengths; aberration parameters can include spherical aberration coefficients and coma gradients; and optical path offset parameters can include the micrometer-level displacement of the mechanical position of the element. Spatial filtering can be a signal processing method to suppress high-frequency components of the signal. For example, it can be achieved by designing a low-pass filter with a cutoff frequency equal to the bandwidth of the characteristic signal after performing a Fourier transform on the parameter sequence, thereby filtering out high-frequency jitter caused by sensor thermal noise or mechanical vibration. Optical state-space mapping can be a mathematical modeling process that converts discrete parameters into a high-dimensional state vector. For example, the light intensity distribution, aberration coefficients, and offset parameters can be arranged into a matrix structure with physical topological relationships through coordinate transformation or feature extraction algorithms. Noise covariance estimation can be a statistical analysis method that quantifies the statistical correlation between parameters. For example, the elements of the covariance matrix between parameters can be calculated using maximum likelihood estimation or Bayesian inference, thereby characterizing the coupling characteristics of aberration noise between different channels. Network interference topology can be a graphical model describing the interference propagation path between optical components. For example, optical valves, lenses, and prisms can be used as nodes, and a weighted directed graph can be constructed using the optical path leakage coefficient or aberration coupling strength as edge weights.

[0042] Spatial filtering, through a combination of Fourier transform and low-pass filter design, effectively improves the signal-to-noise ratio of parameter acquisition. In one specific embodiment, this process first transforms the original parameter sequence to the frequency domain, eliminates noise components above the system characteristic frequency by setting the cutoff frequency to 0.7 times the system characteristic frequency, and finally restores the parameter sequence through inverse transform. Optical state-space mapping is reconstructed through parameter topology relationships. For example, the transmittance parameters of the light valve unit are arranged as row vectors according to the optical path, and the aberration coefficients of the lens group are arranged as column vectors according to the focal length parameters, forming a two-dimensional matrix structure with spatial correlation. Noise covariance estimation is performed through statistical hypothesis testing. For example, assuming that aberration noise follows a multivariate Gaussian distribution, the noise correlation coefficient between each channel is calculated using the sample covariance matrix of the parameter sequence. The covariance between spherical aberration and coma can reflect the degree of coupling between the two in physical imaging. The construction of the network interference topology is performed through component layout analysis. For example, the leakage intensity between adjacent light valves is used as the edge weight, and the aberration transmission path between the lens group and the prism is used as the directed edge, forming an interference propagation model containing 20 nodes and 45 edges.

[0043] This embodiment achieves a significant improvement in the accuracy of optical path adjustment control by real-time acquisition of multi-dimensional parameters of multiple optical elements and construction of a high-dimensional state matrix, utilizing spatial filtering to eliminate high-frequency noise interference, combining statistical analysis to quantify the coupling characteristics of aberration noise, and constructing a system-level interference propagation model based on the physical layout. Specifically, the improved signal-to-noise ratio of parameter acquisition increases the confidence level of aberration covariance estimation; the construction of the high-dimensional state matrix supports multi-parameter collaborative analysis; and the system-level interference model can accurately locate the path of coupled interference sources. Ultimately, this reduces the error rate of predicting optical path changes to below 30% of traditional methods, providing a more accurate basis for interference source localization and dynamic modeling in subsequent compensation control.

[0044] In one embodiment, optical path changes are predicted based on the aberration matrix and optical path crosstalk matrix to generate predicted optical path parameters. Optical path model simulation is then performed based on these predicted parameters to obtain a multi-dimensional optical path simulation model of the image projector, including: The crosstalk correlation matrix is ​​obtained by weighting the aberration matrix with channel correlation using the optical path crosstalk matrix. The aberration matrix can be a mathematical expression describing the aberration characteristics of each optical path channel. It can be obtained through wavefront distortion measurement or simulation calculation of optical elements. For example, the aberration matrix can include parameters such as spherical aberration coefficients, coma coefficients, or distortion coefficients. The optical path crosstalk matrix can be a weight matrix quantifying the energy leakage or interference intensity between different optical path channels. It can be obtained through optical path interference experiments or numerical simulations. For example, the optical path crosstalk matrix can reflect the proportion of energy transfer caused by diffraction or scattering between adjacent optical paths. The crosstalk correlation matrix can be a comprehensive interference description matrix that integrates the coupling effects of aberration and crosstalk. Its generation process is achieved through matrix multiplication or weighted summation operations. For example, the aberration coefficients of each channel in the aberration matrix are linearly combined with the crosstalk intensity at the corresponding position in the crosstalk matrix to reflect the enhancement or suppression effect of aberration on crosstalk. This technique can be implemented through linear algebra operations, such as matrix multiplication or weighted superposition, to transform the independent models of aberration and crosstalk into a more correlated comprehensive interference model, thereby providing a more comprehensive description of interference characteristics for subsequent predictions.

[0045] Based on the crosstalk correlation matrix, spectral aberration transformation is performed on the real-time optical path parameters to generate an optical path spectrum matrix containing wavelength, light intensity, and aberration coefficients. Real-time optical path parameters can be measured or estimated values ​​of wavelength distribution, light intensity distribution, and aberration coefficients of each channel in the current optical path system. These can be obtained through a spectral analyzer, photoelectric sensor, or numerical simulation. Spectral aberration transformation is a mathematical process that maps multidimensional interference parameters to the wavelength correlation characteristics of optical elements. For example, it can separate the aberration contributions of different bands through Fourier transform, or calculate the optical path difference using the dispersion relationship of material refractive index with wavelength. The optical path spectral matrix can be a multidimensional parameter set integrating wavelength sensitivity, light intensity attenuation coefficient, and aberration variation trend. Its construction process needs to consider the dispersion characteristics of optical elements. For example, it can use spectral decomposition technology to multiply the aberration coefficients corresponding to each wavelength with the channel weights in the crosstalk correlation matrix to generate comprehensive interference parameters containing the wavelength dimension. This technique can be implemented through multidimensional matrix operations and spectral analysis methods. For example, spectral decomposition can be used to decompose real-time optical path parameters into sub-matrices of different bands, and then weighted and fused with the crosstalk correlation matrix to form a structured set of optical path characteristic parameters to support the input requirements of subsequent dynamic prediction.

[0046] By using a dynamic prediction model to perform time-series deduction of the optical path spectrum matrix, the light intensity distribution and aberration change trend of each optical path channel are predicted, and the predicted optical path parameters are obtained. The dynamic prediction model can be a prediction framework based on time series analysis or machine learning algorithms. For example, it can use a Long Short-Term Memory (LSTM) neural network to capture nonlinear temporal characteristics, or utilize a Kalman filter to fuse predicted values ​​with measured sensor data. Time series extrapolation can be achieved by inputting the optical path spectrum matrix and environmental sensor data (such as temperature and humidity) as auxiliary variables, and outputting the future evolution trend of optical path parameters. The predicted optical path parameters can be a set of prediction results containing the expected light intensity values, aberration compensation amounts, and crosstalk suppression thresholds for each channel at future times. This technique can be implemented through state-space modeling or deep learning training. For example, in an LSTM model, the optical path spectrum matrix can be used as the input sequence, and time dependencies can be captured through hidden layers. Alternatively, a state transition equation and observation equation can be established in a Kalman filter, and the prediction trajectory can be corrected by combining environmental data, thereby generating forward-looking optical path parameter prediction results.

[0047] Based on the preset optical path geometry parameters and optical valve response characteristics of the image projector, the predicted optical path parameters are mapped to execution parameters, which include the optical valve driving voltage and the lens displacement. The preset optical path geometric parameters can be the initial configuration values ​​of static optical elements such as lens focal length and prism angle, which can be obtained through optical design software or mechanical structure parameter tables. The optical valve response characteristics can be the nonlinear relationship curve between the driving voltage and transmittance of the optical valve array, which can be obtained through experimental calibration or material property data. Execution parameter mapping can transform the predicted aberration change trend and light intensity distribution into executable control commands. For example, aberration compensation can be mapped to lens group displacement using a lookup table, or light intensity demand can be converted into the duty cycle of the driving voltage using a PWM control algorithm. This technique can be implemented by solving nonlinear equations or by looking up a mapping table. For example, the corresponding driving voltage can be determined by reversely looking up the optical valve response characteristic curve, or the displacement can be calculated based on the linear relationship between lens displacement and aberration compensation. Simultaneously, the physical constraints of the optical elements, such as displacement range and response bandwidth, must be met.

[0048] Based on the execution parameters, the topology of the light valve array, lens group, and prism in the image projector is reconstructed to build a multi-dimensional optical path simulation model containing dynamic optical elements.

[0049] Topology reconstruction can be a process of dynamically adjusting the layout of optical components based on execution parameters, such as moving lens groups via motors, adjusting prism rotation angles, or reconfiguring the drive sequence of the optical valve array. The multi-dimensional optical path simulation model can be a real-time simulation framework integrating dynamic and static optical component parameters, environmental interference models, and optical valve drive characteristics. Its construction process employs a layered modeling approach: the bottom layer calculates the optical path and energy distribution through ray tracing; the middle layer evaluates the statistical characteristics of aberrations and crosstalk through Monte Carlo simulation; and the top layer compares the simulation results with measured data through a feedback loop to optimize model parameters. This technique can be implemented using ray tracing algorithms and numerical simulation software, such as Zemax or CodeV, to calculate the ray path after optical component layout adjustment, and to simulate the system performance under random interference using the Monte Carlo method, ultimately forming a high-fidelity dynamic simulation model that supports real-time updates and closed-loop feedback.

[0050] This embodiment fuses the aberration matrix and the optical path crosstalk matrix into a correlation weight matrix. It then combines spectral decomposition and a dynamic prediction model to generate multi-dimensional optical path characteristic parameters. Dynamic adjustments to the optical component layout are achieved through parameter mapping and topology reconstruction. Furthermore, a real-time updatable simulation framework is constructed based on hierarchical modeling, significantly improving the accuracy of optical path change prediction and the system's dynamic adaptability. This scheme quantifies the coupling effect of multi-dimensional interference factors, combines environmental data and machine learning algorithms to achieve forward-looking prediction of interference trends, and translates the prediction results into specific physical control actions. Simultaneously, the effectiveness of the adjustments is verified through simulation, thereby continuously optimizing optical path parameters in complex environments and effectively improving the geometric accuracy, color consistency, and anti-interference capability of the projected image.

[0051] In one embodiment, the optical valve duty cycle is calculated on the multi-dimensional optical path simulation model to obtain a basic driving sequence. The basic driving sequence is then subjected to optical valve dead-zone compensation to generate an anti-crosstalk driving sequence, including: Channel brightness equalization processing is performed on the multi-dimensional optical path simulation model, and optical path differences are eliminated by gamma correction algorithm to obtain the equalized light intensity matrix; The multi-dimensional optical path simulation model can be a digital optical path system model that includes optical component parameters and optical path losses. It can be constructed using methods such as finite element analysis or Monte Carlo simulation. For example, it includes parameters such as the refractive index distribution of the lens group and the flatness of the reflecting mirror. The gamma correction algorithm can be a mathematical function used to adjust the nonlinear mapping relationship between the input signal and the output light intensity. For example, it uses a power function form, such as a typical value of γ=2.2, to compensate for the perceptual characteristics of the human eye. Optical path differences can be the phenomenon of uneven light intensity distribution caused by optical component processing errors or different path lengths. For example, this includes light intensity attenuation in the edge region of the lens group or light intensity fluctuations caused by differences in the coating thickness of the reflecting mirror. The balanced light intensity matrix can be a numerical matrix describing the normalized light intensity distribution of each channel. For example, it is generated through iterative calculation or lookup table methods and includes correction coefficients for the driving signals of each channel.

[0052] For example, this can be achieved by comparing the light intensity distribution of each optical path in the simulation model and calculating the difference compensation coefficient matrix. For instance, when the light intensity of a certain channel decreases by 15% due to lens loss, the driving signal strength of that channel is increased in reverse to offset the attenuation. This process can employ numerical iteration or lookup table methods to ensure that each channel outputs a consistent light intensity level under the same driving conditions, thereby eliminating brightness differences between channels.

[0053] Based on the equalized light intensity matrix and the light valve response curve, the linear relationship between the light valve opening / closing time and the light intensity is deduced, and the light valve driving equalization matrix is ​​generated. The light valve response curve can be a function curve describing the relationship between driving voltage, transmittance, and response time. For example, it includes parameters such as rise time, fall time, and steady-state transmittance. The light valve drive equalization matrix can be a numerical matrix storing the mapping relationship between driving parameters and light intensity requirements. For example, it is generated by fitting differential equations or the least squares method, and includes pulse width adjustment coefficients and voltage amplitude correction values ​​for each channel.

[0054] For example, this can be achieved by combining the equalization light intensity matrix with the light valve response curve to establish a quantitative relationship between the driving signal parameters and the actual output light intensity. For instance, when the target light intensity needs to be increased by 20%, the required extended on-time or increased driving voltage can be calculated based on the response curve. This process can employ multivariate linear regression or numerical fitting methods to convert the light intensity requirement into a specific combination of driving parameters, ultimately forming a parameter mapping relationship stored in the matrix.

[0055] Multidimensional duty cycle calculation is performed on the light valve drive equalization matrix, and a basic drive sequence is generated in combination with the frame rate requirements of the image projector. The multi-dimensional duty cycle calculation can be an optimization process that simultaneously satisfies brightness equalization, frame rate constraints, and driving hardware limitations. For example, it involves pulse width allocation and time interval planning. The frame rate requirement of the image projector can be a system refresh rate requirement such as 60Hz or 120Hz, corresponding to single frame durations of 16.67ms and 8.33ms respectively. The basic driving sequence can be a parameter sequence containing the driving timing, voltage amplitude, and duration of each channel. For example, it can be generated through dynamic programming or linear programming algorithms, including pulse start and end times and voltage waveform parameters.

[0056] For example, the theoretical driving parameters for each channel are first determined based on the driving equalization matrix. Then, the number of available driving cycles per frame is calculated in conjunction with the frame refresh rate. Finally, pulse width and timing are allocated using an optimization algorithm. For instance, when a channel needs to output higher light intensity, a longer pulse width is allocated while adjusting the interval between adjacent pulses to avoid exceeding the frame rate limit. This process ensures that the total driving time does not exceed the duration of a single frame, ultimately generating a basic driving sequence containing the driving parameters.

[0057] Perform optical valve edge switching conflict detection on the basic drive sequence, identify synchronous switching interference points of adjacent optical valve units, and generate timing scheduling parameters; The optical valve edge switching conflict can be an interference phenomenon caused by the overlap of drive signals from adjacent channels on the time axis. For example, it can include two optical valve units closing successively within 1 ms. The timing scheduling parameters can be control parameters used to adjust the drive timing. For example, they can include conflict region delay values ​​or pulse interval extension.

[0058] For example, this can be achieved by analyzing the start and end times of the drive pulses of each channel in the basic drive sequence and using a time window scanning method to identify overlapping regions. For instance, when a switch between adjacent channel drive signals is detected within a 0.5ms interval, the degree of conflict is quantified and the conflict point is marked. The timing scheduling parameters are generated using a conflict avoidance algorithm, such as graph theory path optimization or heuristic search methods, to determine the drive channels that need adjustment and the conflict regions, ultimately generating timing scheduling parameters that include delay or interval extension instructions.

[0059] Dead time compensation is performed on the basic drive sequence based on timing scheduling parameters, non-overlapping drive intervals are added, and anti-crosstalk drive sequences are generated.

[0060] Dead time compensation can be a technique for eliminating signal interference through timing-level adjustments, such as inserting a small delay or extending the non-driving interval. The non-overlapping driving interval can be the isolation time between adjacent driving pulses; its setting needs to balance anti-interference requirements and system efficiency, such as extending the 0.5ms interval to 1.2ms. The anti-crosstalk driving sequence can be the final driving parameter sequence after timing optimization; for example, it is generated by dynamically adjusting the compensation amount through adaptive PID control or fuzzy logic algorithms.

[0061] For example, this can be achieved through the following steps: First, determine the drive channels and conflict areas that need adjustment based on timing scheduling parameters; second, ensure complete isolation of drive pulses by inserting delays or extending intervals; and finally, optimize the compensation amount using a dynamic control algorithm. For instance, PID control can be used to adjust the safety interval threshold according to the degree of conflict, eliminating crosstalk effects caused by capacitive coupling or mechanical vibration while ensuring frame rate requirements.

[0062] This embodiment eliminates multi-channel optical path differences through a gamma correction algorithm to generate a balanced light intensity matrix. It then uses the light valve response curve to deduce the driving parameter mapping relationship to form a driving equalization matrix. A basic driving sequence is generated through multi-dimensional duty cycle calculation and frame rate constraints. A time window scanning method is used to identify switching conflicts and generate timing scheduling parameters. Finally, a dynamic compensation algorithm is used to increase the non-overlapping driving interval to generate an anti-crosstalk driving sequence. This achieves the following technical effects: sub-pixel-level light intensity consistency between channels is achieved through brightness equalization processing and multi-parameter collaborative optimization; signal interference during parallel driving of multiple light valves is eliminated through conflict detection and timing scheduling; and driving isolation is strengthened while maintaining frame rate through adaptive compensation, thereby improving image purity and display accuracy in high-resolution multi-channel projection scenarios.

[0063] In one embodiment, the anti-crosstalk driving sequence is phase-quantized to obtain a quantized phase sequence. Based on the quantized phase sequence, phase staggering is performed on the anti-crosstalk driving sequence to obtain a staggering control sequence, including: The anti-crosstalk drive sequence is divided into phase periods, and phase quantization is performed based on the light valve drive period of the image projector to obtain the quantized phase sequence. Phase period division can be a timing reference operation that divides the optical valve driving cycle into multiple time units. This can be achieved through time window division algorithms of equal or unequal lengths. For example, it can include dividing a 1ms driving cycle into 100 10μs phase units. Quantizing the phase sequence can be a discretized sequence that maps the trigger time of the driving signal to the nearest phase interval. Its generation methods include sampling or segmentation algorithms. For example, the physical constraints of the phase interval can be determined by combining the minimum response time of the optical valve and the system clock resolution. This operation improves the time resolution and provides a precise timing reference for subsequent peak shifting.

[0064] The channel distribution density of each phase interval in the quantized phase sequence is statistically analyzed to generate a quantized phase sequence distribution histogram. Channel distribution density can be a quantitative indicator describing the number of active channels within each phase interval. Its calculation includes counting the channels that simultaneously trigger drive signals within each phase interval. A distribution histogram can be a statistical chart visualizing phase resource occupancy, implemented through sliding window statistics or a real-time counter. For example, if five channels trigger simultaneously in a certain phase interval, the corresponding histogram count is 5. This chart helps identify hotspots of phase resource contention, providing data support for heat dissipation management and interference suppression.

[0065] Power grouping of the optical valve channels is performed based on the quantized phase sequence distribution histogram to construct a phase configuration reference system with balanced heat dissipation; The power grouping of the light valve channels can be a set of categories based on drive power characteristics, such as duty cycle, voltage amplitude, or current intensity. For example, they can be divided into high-power, medium-power, and low-power groups. The phase configuration reference system can be a weighted evaluation system combining the distribution histogram and power grouping information. Its construction involves weighted calculation of the heat dissipation load for each phase interval; for example, if multiple high-power channels are already triggered in a certain interval, its weight value increases. This reference system achieves a balanced distribution of heat dissipation load by prioritizing the allocation of low-load phase intervals.

[0066] Based on the phase configuration reference system, the cross-channel phase offset rearrangement of the anti-crosstalk drive sequence is performed so that the drive phase of the high-power channel is dispersed in different period intervals, thereby generating a peak-shifting control sequence. Cross-channel phase offset rearrangement can be a timing adjustment process that reallocates drive trigger times through a dynamic scheduling algorithm, which can be implemented through iterative optimization or greedy algorithms. The staggered control sequence can be the final drive timing sequence that satisfies optical path energy synchronization and optical valve recovery time constraints. For example, three high-power channels can be assigned to the 0-20%, 40-60%, and 80-100% phase intervals, respectively. This operation reduces the risk of instantaneous current spikes and thermal buildup by dispersing the trigger times of high-power channels, while maintaining global timing synchronization.

[0067] This embodiment improves temporal resolution through phase period division and quantization operations, identifies phase resource competition hotspots using distribution histograms, constructs a heat-balanced phase configuration reference system by combining power grouping, and performs cross-channel phase offset rearrangement based on this reference system. This achieves the technical effects of suppressing local overheating, reducing electromagnetic and thermal interference, and improving equipment stability under high load scenarios. Through the coordinated optimization of thermal management and optical control, this solution addresses the problem of traditional phase allocation focusing only on signal interference while neglecting heat dissipation, providing comprehensive technical assurance for the long-term reliability and dynamic response capability of multi-optical-path systems.

[0068] In one embodiment, the peak-shifting control sequence undergoes multi-path temperature compensation and ambient light compensation to obtain a temperature-compensated driving sequence. Based on this temperature-compensated driving sequence, multi-optical path collaborative processing is performed to generate a multi-dimensional control sequence, including: Based on the thermal expansion coefficients of the light valves and lenses in the image projector and real-time temperature sensor data, the peak shaving control sequence is mapped to the optical path offset parameters under the influence of temperature. The coefficient of thermal expansion can be a physical quantity describing the rate of change of a material's volume or length when the temperature changes. It can be obtained from a material property table, such as the linear expansion coefficient of a light valve robotic arm or the volumetric expansion coefficient of a lens material. Real-time temperature sensor data can be the temperature monitoring results of the optical components inside the projector, which can be acquired in real time through a distributed temperature sensing network. The optical path offset parameters under temperature influence can be a set of parameters characterizing the amount of change in the optical path caused by thermal deformation of the optical components.

[0069] By establishing a temperature deformation relationship model, temperature data is substituted into formulas to calculate the displacement of the light valve drive mechanism and the change in the lens curvature radius. For example, the offset of the light valve robotic arm can be calculated using ΔL = α·L0·ΔT, where ΔL is the offset of the light valve robotic arm, α is the coefficient of thermal expansion of the material, L0 is the original length of the robotic arm, and ΔT is the temperature change. Alternatively, the change in lens focal length can be estimated using Δf = f0·(α·ΔT + dn / dT·ΔT / n0), where Δf is the change in lens focal length, f0 is the original focal length of the lens, α is the coefficient of thermal expansion of the material, ΔT is the temperature change, dn / dT is the temperature coefficient of refractive index of the material, and n0 is the original refractive index of the material. This maps the peak-shifting control sequence to optical path offset parameters. This technique, through the combination of thermal expansion coefficient and temperature data, can perform personalized corrections for different material properties and local temperature changes, achieving precise temperature compensation mapping.

[0070] Temperature coefficient compensation is performed on the driving parameters of each channel to correct the response delay of the light valve and the change in lens focal length caused by thermal deformation, and the temperature-compensated driving sequence of each channel is obtained. The optical valve response delay can be a timing deviation between the drive signal and the actual action of the optical valve caused by temperature changes, which can be compensated for by adjusting the timing parameters of the drive signal or the amplitude of the drive voltage. Lens focal length changes can be temperature-induced shifts in the imaging position of the optical system, which can be corrected by adjusting the lens group spacing or digital correction parameters using an electric focusing mechanism. The temperature-compensated drive sequence can be a set of multi-parameter drive commands including drive voltage, pulse width, and timing offset.

[0071] By adjusting the advance trigger time of the drive pulse or dynamically adjusting the drive voltage using a PID control algorithm, for example, the advance amount of the drive signal is increased when the temperature rises to counteract the light valve hysteresis effect. Simultaneously, the electric focusing mechanism is driven to adjust the lens spacing or modify the digital correction parameters to restore the focal length, thereby generating a temperature-compensated drive sequence containing compensation parameters for each channel. This technique ensures that the optical components maintain the optical path and focusing accuracy even under thermal deformation, avoiding image distortion caused by temperature fluctuations.

[0072] By integrating ambient light data collected by the ambient light sensor of the image projector, the gain of the temperature compensation drive sequence is dynamically adjusted to compensate for the influence of ambient light on the projection brightness. The ambient illuminance data can be real-time light intensity measurements of the projection area, which can be obtained through grayscale analysis of a photodiode array or image sensor. Dynamic gain adjustment can be a process of adaptively adjusting the light valve drive parameters based on the ambient light intensity, exemplified by duty cycle adjustment or drive voltage amplitude correction.

[0073] By establishing a mapping relationship between ambient illuminance and projection brightness, and employing adaptive fuzzy control or a neural network model, the light valve duty cycle or driving voltage is increased to enhance luminous flux when the ambient illuminance exceeds a threshold, or driving parameters are reduced to save energy in low-illuminance environments. Simultaneously, considering the visual characteristics of the human eye, contrast is prioritized over pure brightness. This technology combines nonlinear compensation curves with intelligent algorithms to achieve adaptive brightness adjustment under changing ambient light, preventing overexposure or loss of detail in dark areas.

[0074] Multi-optical-path collaborative optimization is performed on the compensated temperature-compensated drive sequence, and the inter-channel light intensity equalization correction coefficient is calculated. The light intensity equalization correction coefficient can be a normalized parameter used to eliminate the output differences of multiple optical paths, and examples include the multiplicative adjustment factor of the driving parameters of each channel.

[0075] A light intensity matrix is ​​constructed by collecting actual light intensity distribution data for each channel. After calculating the relative deviation between channels, a normalization algorithm is used to generate correction coefficients. For example, the ratio of (target light intensity / current light intensity) is used for parameter adjustment. This technology achieves multi-objective optimization through the Lagrange multiplier method or genetic algorithm, controlling system power consumption while satisfying optical path energy matching, effectively solving the problem of uneven brightness across multiple channels.

[0076] Based on the correction coefficient, the light flux output model of each optical path in the image projector is reconstructed to generate a light efficiency compensation sequence. The luminous flux output model can be a mathematical expression describing the relationship between the driving parameters and the actual luminous output, such as a polynomial fitting function or a neural network model. The luminous efficacy compensation sequence can be a set of timing control instructions that includes the optimization results of the driving parameters.

[0077] By using the driving voltage, duty cycle, temperature compensation parameters, and ambient illuminance as inputs, the gradient descent method is employed to iteratively correct the driving parameters, matching the luminous flux value predicted by the model with the sensor feedback data, thereby generating a luminous efficacy compensation sequence. This technique, through data-driven model reconstruction and iterative optimization, ensures that the error between the luminous flux output and the target value is minimized.

[0078] The light effect compensation sequence is time-domain synchronized to ensure alignment with the frame rate and image processing timing of the image projector, thereby generating a multi-dimensional control sequence.

[0079] Among these, time-domain synchronization processing can be a technical means to eliminate compensation calculation delay and ensure synchronization between the driving signal and image refresh, exemplified by timestamp alignment and pulse width interpolation. Multidimensional control sequences can be a comprehensive control instruction set integrating temperature compensation, ambient light gain, light intensity equalization, and timing synchronization parameters.

[0080] By aligning the drive pulse timestamp with the system clock, interpolating non-integer period signals, and inserting synchronization signals between multiple channels, and exemplarily employing timestamp calibration and real-time output from a digital signal processor, inter-frame jitter and image tearing are eliminated. This technique achieves seamless integration of multi-dimensional control parameters with system timing through a combination of hardware timing control and algorithm compensation.

[0081] This embodiment achieves significant improvements in temperature deformation compensation accuracy, elimination of multi-optical path brightness differences, and ensures timing synchronization and image stability by mapping optical path offset parameters based on thermal expansion coefficient and temperature data, compensating for the temperature coefficient of driving parameters and focal length correction, dynamically adjusting the ambient illuminance driving gain, coordinating the optimization of multi-channel light intensity equalization, iteratively reconstructing the luminous flux output model, and synchronizing the compensation sequence with the clock signal in the time domain. This method can maintain high-precision optical path control and image output quality of the projection system even in scenarios with drastic temperature fluctuations or frequent changes in ambient light, and is particularly suitable for applications such as virtual reality where timing synchronization requirements are stringent.

[0082] In one embodiment, the optical path adjustment control of the image projector is performed according to a multi-dimensional control sequence, while feedback light intensity and aberration data are collected. Based on the feedback light intensity and aberration data, the multi-dimensional optical path simulation model is optimized to perform multi-dimensional adaptive adjustment of the image projector's optical path, including: The multidimensional control sequence is converted into hardware-executable signals for the image projector, including light valve drive voltage signals and lens servo motor control signals. The multidimensional control sequence can be a set of digital instructions containing optical path adjustment parameters, which can be generated by an algorithm or obtained through external input. For example, the multidimensional control sequence may include a light valve deflection angle sequence, a lens displacement sequence, etc. The hardware-executable signal can be a physical quantity signal capable of directly driving the optical element. For example, the light valve drive voltage signal can be a pulse width modulation (PWM) waveform or a sinusoidal waveform electrical signal, and the lens servo motor control signal can be a pulse width modulation signal or an analog voltage signal. The signal conversion module can be an electronic device that converts digital instructions into physical signals. For example, a digital-to-analog converter (DAC) or a pulse generator can be used to generate a drive voltage waveform that conforms to the material properties of the light valve.

[0083] The light valve and lens group of the image projector are driven by hardware-executable signals to simultaneously collect light intensity distribution and aberration data at the center and edge of the projected image, forming feedback light intensity and aberration data. A light valve can be an optical element that changes the light path through an electric field or mechanical action. For example, a liquid crystal light valve or a microelectromechanical system (MEMS) mirror can adjust the direction of the light path in response to a voltage signal. A lens assembly can be a collection of movable or deformable optical elements. For example, a tilting lens driven by a servo motor or a thermo-deformable lens can compensate for light path offset. A photoelectric sensor array can be a light intensity detection device distributed over the projection area. For example, a uniformly arranged array of photodiodes or a CMOS image sensor can capture the brightness gradient distribution. A wavefront sensor can be a device for measuring optical wavefront distortion. For example, a Shack-Hartmann sensor or a focal spot analyzer can quantify higher-order aberrations such as spherical aberration and coma. Synchronous acquisition can be achieved by coordinating the drive signal and the trigger signal for data acquisition through a timing controller, ensuring the correspondence between the feedback data and the current optical path state.

[0084] The feedback light intensity and aberration data are compared with the theoretical output of the multi-dimensional optical path simulation model to calculate the comparison parameters, which include light intensity uniformity deviation and aberration residual value. A multi-dimensional optical path simulation model can be a mathematical model describing the response characteristics of an optical system. For example, a model built based on ray tracing algorithms or finite element analysis can predict light intensity distribution and aberration characteristics. Light intensity uniformity deviation can be a quantitative indicator characterizing the difference between the actual and theoretical light intensity distribution. For example, it can be achieved by calculating the percentage difference in brightness between the center and edge regions of the projected image. Aberration residual value can be a quantitative parameter characterizing the difference between the actual aberration and the theoretical prediction. For example, it can be achieved by expanding the measured aberration data using Zernike polynomials and calculating the difference between each term and the predicted value from the simulation model. Pixel-level comparison can be a method of comparing the values ​​of each pixel. For example, it can be achieved by performing pixel-by-pixel subtraction between the measured light intensity distribution map and the simulation prediction map to generate a deviation distribution map.

[0085] Based on the comparison parameters, the optical valve response parameters and lens deformation coefficient in the multi-dimensional optical path simulation model are adaptively corrected, the parameters of the multi-dimensional optical path simulation model are updated, a closed-loop adjustment is formed, and the multi-dimensional optical path adaptive adjustment of the image projector is realized.

[0086] The light valve response parameters can be model parameters describing the relationship between the driving voltage and the actual action of the light valve, such as the slope or hysteresis coefficient of the voltage-transmittance transfer function. The lens deformation coefficient can be a parameter characterizing the relationship between the physical deformation of the lens and the external control signal, such as the temperature deformation sensitivity coefficient or the material's thermal expansion coefficient. The least squares method can be a mathematical method that optimizes model parameters by minimizing the sum of squared errors, such as correcting the nonlinear relationship between the light valve driving voltage and the actual deflection angle. The Bayesian optimization algorithm can be a parameter search method based on a probabilistic model, such as dynamically adjusting the prior distribution of the lens deformation coefficient to approximate the true value. Kalman filtering can be a recursive algorithm that combines prediction and measurement data to suppress noise, such as smoothing random fluctuations during parameter updates. The sliding window averaging algorithm can be a numerical smoothing method based on a time window, such as eliminating the interference of instantaneous noise on parameter correction. Closed-loop regulation can be a control structure that continuously iterates parameter correction and control execution, such as continuously interacting control commands and feedback data by re-inputting the updated model parameters into the control sequence generation module.

[0087] This embodiment converts multidimensional control sequences into driving signals that conform to hardware characteristics, utilizes photoelectric sensors and wavefront sensors to synchronously collect multi-region feedback data, quantizes light intensity and aberration deviations based on pixel-level comparison and Zernike polynomial expansion, combines least squares method, Bayesian optimization and other algorithms to correct model parameters online, and suppresses noise interference through Kalman filtering, ultimately forming a continuously iterative closed-loop adjustment mechanism. This can significantly improve the stability of projected image quality in extreme environments, shorten feedback optimization response delay, and enhance the accuracy of optical path adjustment and system robustness.

[0088] Furthermore, this embodiment of the invention also proposes a storage medium storing a multi-dimensional optical path adaptive adjustment control program for an image projector. When the multi-dimensional optical path adaptive adjustment control program for an image projector is executed by a processor, it implements the steps of the multi-dimensional optical path adaptive adjustment control method for an image projector as described above.

[0089] In addition, refer to Figure 3 This invention also proposes a multi-dimensional optical path adaptive adjustment and control system for an image projector, the multi-dimensional optical path adaptive adjustment and control system for an image projector comprising: The parameter parsing module 10 is used to acquire multiple real-time optical path parameters in the image projector, perform aberration noise covariance estimation on the multiple real-time optical path parameters to obtain an aberration matrix, and perform optical element crosstalk analysis on the multiple real-time optical path parameters to generate an optical path crosstalk matrix. The prediction modeling module 20 is used to predict optical path changes in real-time optical path parameters based on the aberration matrix and the optical path crosstalk matrix, generate predicted optical path parameters, and perform optical path model simulation based on the predicted optical path parameters to obtain a multi-dimensional optical path simulation model of the image projector. The drive compensation module 30 is used to calculate the optical valve duty cycle of the multi-dimensional optical path simulation model to obtain a basic drive sequence, and to perform optical valve dead zone compensation processing on the basic drive sequence to generate an anti-crosstalk drive sequence. The phase allocation module 40 is used to perform phase quantization on the anti-crosstalk driving sequence to obtain a quantized phase sequence, and to perform phase staggering allocation on the anti-crosstalk driving sequence based on the quantized phase sequence to obtain a staggering control sequence. The temperature compensation coordination module 50 is used to perform multi-path temperature compensation and ambient light compensation on the peak shaving control sequence to obtain a temperature compensation driving sequence, and to perform multi-optical path coordination processing on the temperature compensation driving sequence to generate a multi-dimensional control sequence. The feedback optimization module 60 is used to perform optical path adjustment control of the image projector according to the multi-dimensional control sequence, and at the same time collect feedback light intensity and aberration data, and perform feedback optimization on the multi-dimensional optical path simulation model based on the feedback light intensity and aberration data, so as to execute multi-dimensional optical path adaptive adjustment of the image projector.

[0090] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or system. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or system that includes that element.

[0091] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. Among the listed system components, several of these systems may be specifically embodied by the same hardware item. The use of terms such as "first," "second," and "third," etc., does not indicate any order and can be interpreted as names.

[0092] Through the above description of the embodiments, those skilled in the art can clearly understand that the methods of the above embodiments can be implemented by means of software plus necessary general-purpose hardware platforms. Of course, they can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as a read-only memory image (ROM) / random access memory (RAM), magnetic disk, optical disk), and includes several instructions to cause a terminal user device (which may be a mobile phone, computer, server, air conditioner, or network user device, etc.) to execute the methods described in the various embodiments of the present invention.

[0093] The above are merely preferred embodiments of the present invention and do not limit the scope of the patent. Any equivalent structural or procedural transformations made based on the description and drawings of the present invention, or direct or indirect applications in other related technical fields, are similarly included within the scope of patent protection of the present invention.

Claims

1. A multi-dimensional light path adaptive adjustment control method for an image projector, characterized in that, The method comprises: Obtaining multi-path real-time optical path parameters in the image projector, performing aberration noise covariance estimation on the multi-path real-time optical path parameters to obtain an aberration matrix, and performing optical element crosstalk analysis on the multi-path real-time optical path parameters to generate an optical path crosstalk matrix; Based on the aberration matrix and the optical path crosstalk matrix, predicting optical path changes of the real-time optical path parameters to generate predicted optical path parameters, and performing optical path model simulation according to the predicted optical path parameters to obtain a multi-dimensional optical path simulation model of the image projector; Performing optical valve duty cycle calculation on the multi-dimensional optical path simulation model to obtain a basic driving sequence, and performing optical valve dead zone compensation processing on the basic driving sequence to generate an anti-crosstalk driving sequence; Performing phase quantization on the anti-crosstalk driving sequence to obtain a quantized phase sequence, and performing phase peak-missing allocation on the anti-crosstalk driving sequence based on the quantized phase sequence to obtain a peak-missing control sequence; Performing multi-path temperature compensation and ambient light compensation on the peak-missing control sequence to obtain a temperature compensation driving sequence, and performing multi-optical path cooperative processing according to the temperature compensation driving sequence to generate a multi-dimensional control sequence; According to the multi-dimensional control sequence, the optical path adjustment control of the image projector is performed, and feedback light intensity and aberration data are collected simultaneously, and the multi-dimensional optical path simulation model is optimized based on the feedback light intensity and aberration data to perform multi-dimensional optical path adaptive adjustment of the image projector.

2. The multi-dimensional light path adaptive adjustment control method of the image projector according to claim 1, wherein, The method comprises: Obtaining multi-path real-time optical path parameters in the image projector, performing aberration noise covariance estimation on the multi-path real-time optical path parameters to obtain an aberration matrix, and performing optical element crosstalk analysis on the multi-path real-time optical path parameters to generate an optical path crosstalk matrix, comprising: Obtaining real-time light intensity, aberration and optical path offset parameters of the optical valve unit, lens group and prism in the image projector to form multi-path real-time optical path parameters; Performing spatial filtering processing on the multi-path real-time optical path parameters to filter out high-frequency noise to obtain filtered optical path parameters; Performing optical state space mapping on the filtered optical path parameters to construct an optical path state matrix containing light intensity distribution and aberration coefficient; Performing noise covariance estimation on the optical path state matrix to extract noise correlation of each optical path channel to obtain an aberration matrix; 3. The method of claim 1, wherein the method further comprises: Based on the optical element layout of the image projector, a network interference topology structure is constructed, and optical element crosstalk analysis is performed on the multi-path real-time optical path parameters, the optical element crosstalk includes light leakage between optical valves and lens aberration coupling interference, and an optical path crosstalk matrix is generated. The method comprises: Through the optical path crosstalk matrix, the channel correlation of the aberration matrix is weighted to obtain a crosstalk correlation matrix; Based on the crosstalk correlation matrix, spectral aberration conversion is performed on the real-time optical path parameters to generate an optical path spectrum matrix containing wavelength, light intensity and aberration coefficient; Using a dynamic prediction model, the optical path spectrum matrix is time-series deduced to predict the light intensity distribution and aberration change trend of each optical path channel to obtain predicted optical path parameters; According to the preset optical path geometry parameters of the image projector, the light valve response characteristics, the predicted optical path parameters are mapped into execution parameters, the execution parameters including a light valve driving voltage and a lens displacement amount; Based on the execution parameters, a light valve array, a lens group and a prism in the image projector are topologically reconstructed to construct a multi-dimensional optical path simulation model containing a dynamic optical element.

4. The method of claim 1, wherein the method further comprises: determining a plurality of light paths of the plurality of light paths of the image projector; and adjusting the plurality of light paths of the plurality of light paths of the image projector based on the determined plurality of light paths of the plurality of light paths of the image projector. The multi-dimensional optical path simulation model is subjected to a light valve duty cycle calculation to obtain a basic driving sequence, and the basic driving sequence is subjected to a light valve dead zone compensation processing to generate an anti-crosstalk driving sequence, including: The multi-dimensional optical path simulation model is subjected to a channel brightness equalization processing to eliminate optical path differences through a gamma correction algorithm to obtain an equalized light intensity matrix; Based on the equalized light intensity matrix and a light valve response curve, a linear relationship between light valve opening / closing time and light intensity is deduced to generate a light valve driving equalization matrix; The light valve driving equalization matrix is subjected to a multi-dimensional duty cycle calculation to generate a basic driving sequence in combination with a frame frequency requirement of the image projector; The basic driving sequence is subjected to a light valve edge switching conflict detection to identify synchronous switching interference points of adjacent light valve units to generate timing scheduling parameters; Based on the timing scheduling parameters, a dead zone time compensation is performed on the basic driving sequence to increase a non-overlapping driving interval to generate an anti-crosstalk driving sequence.

5. The method of claim 1, wherein the method further comprises: determining a plurality of light paths of the plurality of light paths of the light beam; and adjusting the plurality of light paths of the light beam to a plurality of adjusted light paths of the light beam. The anti-crosstalk driving sequence is subjected to phase quantization to obtain a quantized phase sequence, and the anti-crosstalk driving sequence is subjected to phase peak avoidance allocation based on the quantized phase sequence to obtain a peak avoidance control sequence, including: The anti-crosstalk driving sequence is subjected to phase period division, and phase quantization is performed based on a light valve driving period of the image projector to obtain a quantized phase sequence; Distribution density of each phase interval in the quantized phase sequence is counted to generate a quantized phase sequence distribution histogram; Based on the quantized phase sequence distribution histogram, power grouping is performed on the light valve channels to construct a phase configuration reference system with balanced heat dissipation; According to the phase configuration reference system, a cross-channel phase offset rearrangement is performed on the anti-crosstalk driving sequence to disperse driving phases of high-power channels in different period intervals to generate a peak avoidance control sequence.

6. The method of claim 1, wherein the method further comprises: The peak avoidance control sequence is subjected to multi-path temperature compensation and ambient light compensation to obtain a temperature compensation driving sequence, and multi-path collaborative processing is performed based on the temperature compensation driving sequence to generate a multi-dimensional control sequence, including: Based on a thermal expansion coefficient and real-time temperature sensor data of a light valve and a lens in the image projector, the peak avoidance control sequence is mapped into optical path offset parameters under the influence of temperature; Temperature coefficient compensation is performed on each channel driving parameter to correct light valve response delay and lens focal length change caused by thermal deformation to obtain a temperature compensation driving sequence of each channel; Dynamic gain adjustment is performed on the temperature compensation driving sequence by fusing ambient illuminance data collected by an ambient light sensor of the image projector to compensate for the influence of ambient light on projection brightness; Multi-path collaborative optimization is performed on the compensated temperature compensation driving sequence to calculate a light intensity equalization correction coefficient between channels; Based on the correction coefficient, a luminous flux output model of each optical path in the image projector is reconstructed to generate a light efficiency compensation sequence; The light efficiency compensation sequence is subjected to time domain synchronization processing to ensure alignment with the frame rate and image processing timing of the image projector, and a multi-dimensional control sequence is generated.

7. The method of claim 1, wherein the method further comprises: determining a plurality of light paths of the plurality of light paths of the light beam; and adjusting the plurality of light paths of the light beam to a plurality of adjusted light paths of the light beam. The light path adjustment control of the image projector is performed according to the multi-dimensional control sequence, and feedback light intensity and aberration data are collected, and the multi-dimensional light path simulation model is subjected to feedback optimization based on the feedback light intensity and aberration data to perform multi-dimensional light path adaptive adjustment of the image projector, including: The multi-dimensional control sequence is converted into a hardware executable signal of the image projector, and the hardware executable signal includes a light valve driving voltage signal and a lens servo motor control signal; The light valve and lens group of the image projector are driven through the hardware executable signal, and the light intensity distribution and aberration residual data of the center and edge of the projection picture are collected synchronously to form feedback light intensity and aberration data; The feedback light intensity and aberration data are compared with the theoretical output of the multi-dimensional light path simulation model, and comparison parameters including light intensity uniformity deviation and aberration residual value are calculated; The light valve response parameters and lens deformation coefficients in the multi-dimensional light path simulation model are adaptively corrected based on the comparison parameters, the multi-dimensional light path simulation model parameters are updated, a closed loop adjustment is formed, and multi-dimensional light path adaptive adjustment of the image projector is realized.

8. A multi-dimension light path adaptive adjustment control system for an image projector, characterized in that, The image projector multi-dimensional light path adaptive adjustment control system includes: A parameter analysis module is configured to obtain multi-path real-time light path parameters in an image projector, estimate aberration noise covariance of the multi-path real-time light path parameters to obtain an aberration matrix, and analyze optical element crosstalk of the multi-path real-time light path parameters to generate a light path crosstalk matrix. A prediction modeling module is configured to predict light path changes of real-time light path parameters based on the aberration matrix and the light path crosstalk matrix to generate predicted light path parameters, and simulate a light path model according to the predicted light path parameters to obtain a multi-dimensional light path simulation model of the image projector. A driving compensation module is configured to calculate a light valve duty cycle of the multi-dimensional light path simulation model to obtain a basic driving sequence, and perform light valve dead zone compensation processing on the basic driving sequence to generate an anti-crosstalk driving sequence. A phase allocation module is configured to quantize a phase of the anti-crosstalk driving sequence to obtain a quantized phase sequence, allocate the anti-crosstalk driving sequence based on the quantized phase sequence to obtain a peak-missing control sequence, and perform multi-path temperature compensation and ambient light compensation on the peak-missing control sequence to obtain a temperature compensation driving sequence. A feedback optimization module is configured to perform light path adjustment control of the image projector according to the multi-dimensional control sequence, collect feedback light intensity and aberration data, and perform feedback optimization on the multi-dimensional light path simulation model based on the feedback light intensity and aberration data to perform multi-dimensional light path adaptive adjustment of the image projector. ​ 9. A multi-dimension light path adaptive adjusting control device for image projector, characterized in that, The device comprises a memory, a processor, and an image projector multi-dimensional light path adaptive adjustment control program stored on the memory and executable on the processor, and the image projector multi-dimensional light path adaptive adjustment control program is configured to implement the steps of the image projector multi-dimensional light path adaptive adjustment control method according to any one of claims 1 to 7.

10. A storage medium, characterized by The storage medium stores an image projector multi-dimensional light path adaptive adjustment control program, and the image projector multi-dimensional light path adaptive adjustment control program is executed by the processor to implement the steps of the image projector multi-dimensional light path adaptive adjustment control method according to any one of claims 1 to 7.

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