Method and device for regulating and controlling wavelength of Micro-LED micro display device with ultrahigh pixel density

Abstract

The invention relates to the technical field of micro display devices, and discloses an ultrahigh pixel density Micro-LED micro display device wavelength regulation and control method and device, and the method comprises the steps: carrying out the hierarchical modeling of the device-level characteristics and system-level characteristics of a Micro-LED array, and obtaining a prediction control model; performing static bandwidth expansion processing on the spectral signal and the light-emitting area to obtain a target wavelength signal; equivalent circuit modeling is carried out on the Micro-LED drive circuit and the control network, and a wavelength control equivalent circuit model is obtained; performing modal decomposition and multi-target parameter optimization to obtain a wavelength regulation and control strategy; and inputting the wavelength regulation and control strategy into a control system for closed-loop control, comparing the acquired spectral measurement signal with a preset target value, calculating a deviation value, and outputting a control quantity to the Micro-LED drive circuit. Therefore, real-time accurate control of wavelength regulation and control is realized, and stable operation and display quality of the Micro-LED micro display device with ultrahigh pixel density are ensured.

Description

Technical Field

[0001] The present application relates to the technical field of micro-display devices, and in particular to a wavelength control method and device for an ultra-high pixel density Micro-LED micro-display device. Background Art

[0002] As an important development direction of the new generation of display technology, ultra-high pixel density Micro-LED micro-display devices have significant advantages in high brightness, low power consumption, and long life. However, in practical applications, due to the complexity of the device structure and the increase in pixel density, the precise control of wavelength output faces severe challenges. Especially under high pixel density conditions, the crosstalk effect between adjacent pixels is significantly enhanced, resulting in a decrease in wavelength stability and affecting display quality.

[0003] Traditional wavelength control methods mainly rely on direct wavelength measurement equipment, which not only requires high cost investment, but also affects device performance. At the same time, although the existing single spectral sensor can obtain high-frequency dynamic spectral components, it is limited by bandwidth limitations and cannot accurately obtain low-frequency basic wavelength components, which restricts the accuracy and stability of wavelength control. Summary of the invention

[0004] The present application provides a method and device for wavelength regulation of an ultra-high pixel density Micro-LED micro-display device, thereby realizing real-time and precise control of wavelength regulation, and ensuring stable operation and display quality of the ultra-high pixel density Micro-LED micro-display device.

[0005] In a first aspect, the present application provides a method for wavelength control of an ultra-high pixel density Micro-LED micro display device, the method comprising: The device-level characteristics and system-level characteristics of the Micro-LED array are hierarchically modeled to obtain a predictive control model; Performing static bandwidth expansion processing on the spectral signal and the luminous area according to the predictive control model to obtain a wavelength dynamic component and a wavelength static component, and fusing the wavelength dynamic component with the wavelength static component to obtain a target wavelength signal; Performing equivalent circuit modeling on the Micro-LED driving circuit and control network based on the target wavelength signal to obtain a wavelength control equivalent circuit model; Performing modal decomposition processing on the wavelength control equivalent circuit model to obtain dominant modes and modal coupling relationships; Performing multi-objective parameter optimization based on the dominant mode and the mode coupling relationship to obtain a wavelength regulation control strategy; The wavelength regulation control strategy is input into the control system for closed-loop control, the collected spectral measurement signal is compared with the preset target value, the deviation value is calculated and the control amount is output to the Micro-LED driving circuit.

[0006] The second aspect of the present application provides a wavelength control device for an ultra-high pixel density Micro-LED micro display device, the wavelength control device for an ultra-high pixel density Micro-LED micro display device comprising: A hierarchical modeling module is used to perform hierarchical modeling on the device-level characteristics and system-level characteristics of the Micro-LED array to obtain a predictive control model; A fusion module, used for performing static bandwidth expansion processing on the spectral signal and the luminous area according to the predictive control model to obtain a wavelength dynamic component and a wavelength static component, and fusing the wavelength dynamic component with the wavelength static component to obtain a target wavelength signal; An equivalent circuit modeling module, used to perform equivalent circuit modeling on the Micro-LED driving circuit and the control network based on the target wavelength signal to obtain a wavelength control equivalent circuit model; A modal decomposition module, used for performing modal decomposition processing on the wavelength control equivalent circuit model to obtain dominant modes and modal coupling relationships; An optimization module, used for performing multi-objective parameter optimization based on the dominant mode and the mode coupling relationship to obtain a wavelength regulation control strategy; The control module is used to input the wavelength regulation control strategy into the control system for closed-loop control, compare the collected spectral measurement signal with the preset target value, calculate the deviation value and output the control amount to the Micro-LED driving circuit.

[0007] The third aspect of the present application provides an electronic device, comprising: a memory and at least one processor, wherein the memory stores instructions; the at least one processor calls the instructions in the memory so that the electronic device executes the above-mentioned ultra-high pixel density Micro-LED micro display device wavelength control method.

[0008] The fourth aspect of the present application provides a computer-readable storage medium, which stores instructions. When the computer-readable storage medium is run on a computer, it enables the computer to execute the above-mentioned ultra-high pixel density Micro-LED micro display device wavelength control method.

[0009] Compared with the prior art, the present application has the following beneficial effects: by constructing a hierarchical predictive control model, the collaborative modeling of device-level and system-level characteristics is realized, the crosstalk effect problem under high pixel density conditions is effectively solved, and the accuracy of wavelength control is improved. By adopting the static bandwidth extension technology, the accurate extraction and fusion of the dynamic component and the static component of the wavelength are successfully realized, the problem of bandwidth limitation of the traditional single spectral sensor is overcome, and the wavelength measurement accuracy is improved. An equivalent circuit model of wavelength control is established. Through multi-time scale dynamic analysis, the cross-time scale coupling mechanism of wavelength control and luminous intensity is revealed. The modal decomposition technology is used to accurately identify the dominant mode and modal coupling relationship, and the accurate description of multi-time scale dynamic characteristics is realized, which improves the pertinence of the control strategy. The introduction of multi-objective parameter optimization method, combined with the self-recovery mechanism, significantly improves the robustness and reliability of wavelength control, and effectively copes with the failure and communication interruption of the spectral measurement unit. Through the implementation of the closed-loop control strategy, real-time and accurate control of wavelength control is achieved, ensuring the stable operation and display quality of the ultra-high pixel density Micro-LED micro-display device. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0011] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0012] Figure 1 It is a schematic flow chart of a wavelength control method for an ultra-high pixel density Micro-LED micro-display device provided by an embodiment of the present invention; Figure 2 It is a schematic block diagram of the structure of a wavelength control device for an ultra-high pixel density Micro-LED micro-display device provided by an embodiment of the present invention; Figure 3 It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0013] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0014] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0015] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0016] It should be further understood that the term "and / or" used in the specification and appended claims of this application refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations. Figure 1 , an embodiment of the wavelength control method of the ultra-high pixel density Micro-LED micro display device in the embodiment of the present application includes: Step 100: hierarchically modeling the device-level characteristics and system-level characteristics of the Micro-LED array to obtain a predictive control model; It is understandable that the execution subject of the present application can be an ultra-high pixel density Micro-LED micro-display device wavelength control device, or a terminal or a server, which is not limited here. The present application embodiment takes the server as the execution subject as an example for explanation.

[0017] Specifically, the wavelength output data in the Micro-LED array is subjected to crosstalk effect analysis to obtain the pixel-to-pixel crosstalk matrix, which can characterize the degree of wavelength interference between adjacent pixels. The historical spectral data collected by the spectral measurement unit is input into the predictor for time series analysis to obtain a multi-dimensional spectral feature sequence. By analyzing the multi-dimensional feature sequence, the spectral variation law of the Micro-LED array under different working conditions is extracted. Device-level modeling of the multi-dimensional spectral feature sequence is performed based on the pixel-to-pixel crosstalk matrix. Device-level modeling not only needs to consider the photoelectric characteristics of each individual pixel, but also the spectral interaction between adjacent pixels. Through modeling, a device-level prediction model is obtained to help predict the wavelength output characteristics of the Micro-LED array under given control conditions. The robustness analysis of the device-level prediction model is performed to identify the unstable factors that may occur in the system during the wavelength regulation process, and the wavelength stability constraint conditions are obtained to ensure that the system can still operate stably under various external interferences and uncertain factors. The constraint conditions provide necessary boundary conditions for system-level modeling to ensure that the system can maintain the stability of wavelength output under various environmental changes. The wavelength stability constraint conditions are input into the system-level modeling unit to obtain a system-level wavelength regulation model. The model considers the wavelength output characteristics at the device level, and comprehensively considers factors such as the spectral response, environmental changes, and temperature fluctuations of the entire display system to ensure that the system can achieve precise wavelength control in a dynamic environment. An event trigger mechanism is constructed according to the working status of the spectral measurement unit, and the control strategy is dynamically adjusted according to the working status of the spectral measurement unit. The event trigger mechanism monitors the spectral data of the system in real time to determine when control intervention is needed, thereby effectively reducing the consumption of system resources and improving control efficiency. On this basis, the signal transmission characteristics of the spectral measurement unit are elastically analyzed in combination with the enhanced trigger strategy. Factors such as delay, noise, and bandwidth in the signal transmission process are analyzed, and elastic control compensation parameters are derived accordingly. The purpose of the elastic control compensation parameters is to dynamically correct non-ideal situations that occur during signal transmission to ensure that the system can always achieve high-precision wavelength control under various operating conditions. The device-level prediction model, the system-level wavelength control model, and the elastic control compensation parameters are integrated to obtain a predictive control model. Through this model, the Micro-LED display system can perform adaptive adjustments based on the real-time collected spectral data and the preset control strategy to ensure the accuracy and stability of the wavelength output.

[0018] Step 200: Perform static bandwidth expansion processing on the spectral signal and the luminous area according to the predictive control model to obtain a wavelength dynamic component and a wavelength static component, and fuse the wavelength dynamic component with the wavelength static component to obtain a target wavelength signal; Specifically, the high-frequency dynamic component is extracted from the wavelength measurement data output by the predictive control model, and the high-frequency components in the wavelength signal are identified. These high-frequency components are related to the dynamic changes in the operation of the device, such as the influence of factors such as driving current and temperature fluctuations. The high-frequency part of the wavelength signal is extracted using a high-frequency signal processing method to obtain a high-frequency spectral component. The high-frequency spectral component is input into a dynamic parameter Kalman filter to perform wavelength signal reconstruction processing. The Kalman filter is a mathematical tool for estimating and predicting the state of a dynamic system, which can effectively extract effective signals from data. Through the processing of the Kalman filter, the wavelength dynamic component is obtained, which reflects the wavelength change characteristics of the Micro-LED array under actual working conditions. The light intensity signal is sampled in the luminous area to obtain the basic spectral data of the luminous area, including the spectral characteristics of the display in a static working state, reflecting the inherent spectral distribution of different light sources and luminous areas. The basic spectral data is input into a low-frequency signal processing unit to reconstruct the static characteristics, extract the wavelength characteristics related to the device in a static state, and obtain the wavelength static component. The static component refers to the spectral characteristics presented by the device when there is no external dynamic change or disturbance. Through the analysis of the low-frequency signal processing unit, the static wavelength information is effectively separated to provide a stable benchmark for subsequent wavelength control. The bandwidth characteristics of the wavelength dynamic component are analyzed. The bandwidth characteristics of the dynamic component are analyzed. These bandwidth characteristics reflect the frequency range and bandwidth limit of the wavelength signal changing over time. By analyzing the bandwidth characteristics of the dynamic component, the dynamic bandwidth compensation coefficient is obtained, which is used to compensate the wavelength static component in the subsequent processing process. The function of the dynamic bandwidth compensation coefficient is to adjust the bandwidth of the static wavelength signal so that it can better adapt to the changes of the dynamic wavelength signal, thereby ensuring the stability and accuracy of the wavelength under different operating conditions. The wavelength static component is compensated for the bandwidth using the dynamic bandwidth compensation coefficient to obtain the compensated wavelength static component, ensuring the coordination of the static component and the dynamic component in frequency and bandwidth, so that the spectrum characteristics of the entire wavelength signal are smoother and more accurate. The wavelength dynamic component and the compensated wavelength static component are fused in the signal time domain to obtain the fused wavelength data, thereby improving the wavelength control accuracy of the system under different working conditions. The fused wavelength data is input into the signal calibration unit for wavelength accuracy calibration. By comparing the deviation between the preset target wavelength value and the actual measured value, the appropriate calibration parameters are output to correct the wavelength signal and obtain the final target wavelength signal.

[0019] Step 300: Perform equivalent circuit modeling on the Micro-LED driving circuit and control network based on the target wavelength signal to obtain a wavelength control equivalent circuit model; It should be noted that the wavelength control time scale analysis is performed on the target wavelength signal to extract the change characteristics of the wavelength signal in the time dimension, that is, the wavelength time series feature sequence. The wavelength time series feature sequence reflects the dynamic change process of the wavelength output at different time points, revealing the influence of factors such as driving current and temperature change on wavelength regulation. In order to analyze the specific influence of the wavelength signal on the luminescence of the Micro-LED array, the wavelength time series feature sequence is input into the luminous intensity analysis unit for time scale analysis of the luminous intensity. The luminous intensity time series feature sequence can capture the relationship between the change in luminous intensity and the change in wavelength at different time scales. The wavelength time series feature sequence and the luminous intensity time series feature sequence are subjected to cross-time scale coupling analysis to analyze the relationship between the wavelength change and the luminous intensity change at different time scales and reveal the coupling characteristics between them. Through the cross-time scale coupling analysis, the time scale coupling characteristics are obtained to describe the common change law of wavelength and luminous intensity in the time domain, so as to ensure that the luminous intensity can be adjusted synchronously when the wavelength is controlled, and to avoid uneven light intensity or brightness fluctuation caused by wavelength change. The time scale coupling characteristics are input into the driving circuit equivalent modeling unit to construct the driving circuit equivalent network structure. The equivalent network structure of the driving circuit is to model the electrical characteristics of the Micro-LED array to describe the interaction between the components in the circuit and the distribution of current and voltage. Through this modeling process, we can understand the influence of each component in the circuit on wavelength regulation, especially in terms of driving current regulation, temperature control, etc. Based on the equivalent network structure of the driving circuit, the dynamic feature extraction of the control network is carried out. The factors such as time delay, noise interference, and bandwidth limitation in the signal transmission process are modeled and analyzed to obtain the equivalent network structure of the control network. The construction of the equivalent network structure of the control network helps to optimize the transmission efficiency of the signal and improve the response speed and accuracy of wavelength control. The dynamic coupling analysis of the equivalent network structure of the driving circuit and the equivalent network structure of the control network is carried out to analyze the mutual coupling relationship between the driving circuit and the control network and reveal their interaction in the dynamic control process. Through the dynamic coupling analysis, the equivalent parameter coupling relationship is obtained to describe how the various parameters in the driving circuit and the control network affect each other and how they are coordinated under different working conditions. The equivalent parameter coupling relationship is input into the integrated circuit modeling unit to construct the integrated equivalent circuit structure. The integrated equivalent circuit structure integrates all the key parameters and coupling relationships of the driving circuit and the control network to form an overall circuit model to simulate the electrical characteristics of the entire Micro-LED display system. After the integrated circuit structure is built, parameter calibration is performed to ensure that the integrated equivalent circuit model can accurately reflect the actual working state of the system. By calibrating the parameters of the integrated circuit structure, the wavelength control accuracy is improved, the output stability of the target wavelength signal is ensured, and efficient and high-precision wavelength control can be achieved in practical applications, and finally a wavelength control equivalent circuit model is obtained.

[0020] Step 400: performing modal decomposition processing on the wavelength control equivalent circuit model to obtain dominant modes and modal coupling relationships; Specifically, a multi-time scale dynamic analysis is performed on the wavelength control equivalent circuit model to obtain the dynamic response data of the wavelength control, which reflects the response of the Micro-LED system to the external driving signal at different time scales, and can reveal the wavelength change law of the system in long-term and short-term control. The wavelength control dynamic response data is input into the luminescence mode analysis unit for timing feature extraction. By analyzing the time series, the luminescence mode sequence in the wavelength control process is extracted. The luminescence mode sequence shows the luminescence state of the Micro-LED array at different time points, reflects the dynamic changes of wavelength control, and reveals the transition law between different luminescence modes. By analyzing these modes, the key timing features in the control process are identified. Based on the luminescence mode sequence, the time scale of wavelength control is decomposed to obtain the wavelength control modal component. The modal component represents the wavelength change law under different wavelength control modes, which helps to clarify the impact of different control strategies on the dynamic response of the system. The wavelength control modal component is input into the spectrum control analysis unit for luminous intensity control analysis. By analyzing the modal component, the cross-time scale correlation characteristics between wavelength control and luminous intensity are identified. As a key analysis tool, the cross-time scale correlation matrix quantifies the relationship between the wavelength control signal and the luminous intensity at different time scales. The dominant mode of the system is extracted from the cross-time scale correlation matrix by principal component analysis. Principal component analysis helps to identify the most representative and influential modes, which dominate the wavelength control process and play a decisive role in the dynamic response of the system. The dominant mode is input into the coupling analysis unit for time scale coupling analysis, and the mutual coupling relationship between the dominant mode and other modes is analyzed to reveal the interaction mode of each mode at different time scales, which helps to identify the key coupling branch characteristics in the wavelength control process. By performing dynamic characteristic analysis on the coupling branch characteristics, it reflects how the modes influence each other at different time scales and how these coupling relationships affect the overall performance of the system. The inter-modal transfer function is input into the correlation analysis unit to calculate the coupling strength. The transfer function describes the degree of mutual influence between the modes in the dynamic system and can quantify the coupling strength between different modes. By calculating the coupling strength, the modal coupling relationship is obtained to determine which modes have the tightest coupling relationship and which modes have less coupling influence.

[0021] Step 500: Perform multi-objective parameter optimization based on the dominant mode and mode coupling relationship to obtain a wavelength regulation control strategy; Specifically, the dominant mode is dynamically decomposed to reveal the key dynamic characteristics of the dominant mode in the wavelength control process, and the wavelength control parameter sequence and the luminous intensity parameter sequence are obtained. The wavelength control parameter sequence describes the change law of the control parameters in the wavelength control process, while the luminous intensity parameter sequence reveals the characteristics of the luminous intensity changing over time. The wavelength control parameter sequence and the luminous intensity parameter sequence are input into the system-level robustness analysis unit for robustness analysis to evaluate the stability and responsiveness of the wavelength control system under various external disturbances and internal parameter changes. Through robustness analysis, a series of robustness constraints are obtained to ensure that the system can still maintain efficient and stable wavelength control in a dynamically changing environment. According to the robustness constraints, the parameters are graded based on the modal coupling relationship. The modal coupling relationship reveals the interaction between different modes in the system and their impact on the dynamic performance of the system. The modal coupling relationship is combined for graded processing, and the optimization space is divided more finely to obtain a multi-objective optimization interval. These optimization intervals represent the optimal range of the system for wavelength control and luminous intensity regulation under different working conditions. The multi-objective optimization interval is input into the optimization algorithm unit for iterative solution. The optimization algorithm unit gradually adjusts the control parameters through an iterative process according to the set objective function to find the optimal control parameter matrix. This process can select a set of optimal control parameters from the parameter space through a large number of calculations to achieve wavelength regulation. The solution of the initial control parameter matrix provides a reasonable starting point for the system. The initial control parameter matrix is ​​state monitored. Through the state monitoring of the spectrum measurement unit, the working state data of the system is obtained in real time, and the state diagnosis features are extracted from it to reflect the deviation between the current system state and the expected target, revealing potential faults or unstable factors. The state diagnosis features are input into the fault prediction unit, and the fault prediction unit calculates the self-recovery parameters based on the feature data. The role of the self-recovery mechanism is to dynamically adjust the system. When a fault or deviation occurs, the control parameters are automatically adjusted to restore the normal working state of the system. Through this process, the fault tolerance and stability of the system are improved, and the self-recovery mechanism parameters are obtained. The initial control parameter matrix and the self-recovery mechanism parameters are fused to obtain the final fusion control strategy. The fusion control strategy is input into the wavelength control unit for parameter calibration. The wavelength control unit applies the fusion control strategy to the actual Micro-LED array, and obtains the wavelength control control strategy by adjusting the control parameters in real time to ensure the accuracy and stability of the wavelength control.

[0022] Step 600: Input the wavelength regulation control strategy into the control system for closed-loop control, compare the collected spectral measurement signal with the preset target value, calculate the deviation value and output the control amount to the Micro-LED driving circuit.

[0023] Specifically, the wavelength control strategy is analyzed for parameters to obtain the key parameters of the control system operation, including various dynamic characteristics related to wavelength control, such as luminous intensity, wavelength stability, and system response time. The wavelength signal collected by the spectrum measurement unit is input into the bandwidth extension module for signal enhancement. The function of the bandwidth extension module is to expand the bandwidth of the wavelength signal, reduce the loss in signal processing, and improve the accuracy and reliability of the wavelength signal. According to the operating parameters of the control system, the difference operation is performed between the processed wavelength signal and the preset target value, and the deviation between the wavelength signal and the preset target value is calculated. The wavelength deviation data reveals the degree of deviation between the current control state of the system and the expected target. Through this deviation data, the part that needs to be adjusted is identified, and a reference is provided for the subsequent control amount calculation. After the wavelength deviation data is input into the control amount calculation unit, the control amount calculation unit matches the strategy according to the set control strategy to obtain the initial drive control amount. The drive control amount reflects the degree of wavelength adjustment required by the system and provides a preliminary control signal for the Micro-LED drive circuit. The working state of the spectrum measurement unit is monitored in real time to obtain the operating state characteristics of the current system. By analyzing the operating state characteristics, abnormalities or deviations in the system can be discovered in time. Based on the operating state characteristics obtained by real-time monitoring, the initial drive control quantity is compensated and the control quantity is adjusted to adapt to the current working conditions. The compensated drive control quantity reflects the target adjustment scheme of the system in the current state. The compensated drive control quantity is input into the fault diagnosis unit for stability analysis. The task of the fault diagnosis unit is to analyze whether the compensated control quantity can effectively ensure the stable operation of the system, especially in the environment of high pixel density display and high precision requirements. By performing stability analysis on the compensated control quantity, potential unstable factors or errors are detected, thereby ensuring the robustness and reliability of the control system and obtaining the target drive control quantity. The target drive control quantity is matched with the Micro-LED drive circuit to ensure that the circuit can accurately perform the wavelength control task. The matching process of the drive circuit includes optimizing and adjusting each part of the circuit so that it can accurately output the required light signal according to the predetermined wavelength control strategy after receiving the control signal. In the final output stage, the adjusted control quantity is transmitted to the Micro-LED drive circuit to complete the wavelength control task of the display.

[0024] In the embodiment of the present application, by constructing a hierarchical predictive control model, the collaborative modeling of device-level and system-level characteristics is realized, the crosstalk effect problem under high pixel density conditions is effectively solved, and the accuracy of wavelength control is improved. By adopting the static bandwidth extension technology, the accurate extraction and fusion of the dynamic component and the static component of the wavelength are successfully realized, the problem of bandwidth limitation of the traditional single spectral sensor is overcome, and the wavelength measurement accuracy is improved. An equivalent circuit model of wavelength control is established. Through multi-time scale dynamic analysis, the cross-time scale coupling mechanism of wavelength control and luminous intensity is revealed. The modal decomposition technology is used to accurately identify the dominant mode and modal coupling relationship, and the accurate description of multi-time scale dynamic characteristics is realized, which improves the pertinence of the control strategy. The introduction of multi-objective parameter optimization method, combined with the self-recovery mechanism, significantly improves the robustness and reliability of wavelength control, and effectively responds to the failure and communication interruption problems of the spectral measurement unit. Through the implementation of the closed-loop control strategy, real-time and accurate control of wavelength control is achieved, ensuring the stable operation and display quality of the ultra-high pixel density Micro-LED micro-display device.

[0025] In a specific embodiment, the process of executing step 100 may specifically include the following steps: The crosstalk effect is analyzed on the wavelength output data in the Micro-LED array to obtain the inter-pixel crosstalk matrix, which represents the degree of wavelength interference between adjacent pixels. The historical spectral data collected by the spectral measurement unit is input into the predictor for time series analysis to obtain a multi-dimensional spectral feature sequence, and the multi-dimensional spectral feature sequence is modeled at the device level based on the crosstalk matrix between pixels to obtain a device-level prediction model; Perform robustness analysis on the device-level prediction model to obtain wavelength stability constraints, and input the wavelength stability constraints into the system-level modeling unit to obtain a system-level wavelength control model; An event trigger mechanism is constructed according to the working state of the spectrum measurement unit to obtain an enhanced trigger strategy, and based on the enhanced trigger strategy, an elastic analysis is performed on the signal transmission characteristics of the spectrum measurement unit to obtain an elastic control compensation parameter; The device-level prediction model, system-level wavelength regulation model and elastic control compensation parameters are integrated to obtain a predictive control model.

[0026] Specifically, the crosstalk effect analysis is performed on the wavelength output data in the Micro-LED array, considering the mutual interference between the pixels in the Micro-LED array, especially the wavelength crosstalk between adjacent pixels. In order to quantify this crosstalk effect, a pixel-to-pixel crosstalk matrix is ​​constructed. This matrix characterizes the degree of wavelength interference between each pair of adjacent pixels and is represented by a symmetric matrix, in which each element represents the degree of interference between a pixel and its adjacent pixel, denoted as ,in and Indicates the pixel number. Elements Calculated by the following formula: ; in It's pixels and pixels The interference power between It's pixels The complete pixel-to-pixel crosstalk matrix is ​​obtained by calculating the crosstalk effect between all pixel pairs in the entire array. , which can reflect how the spectral signal is disturbed by the light emission of adjacent pixels under different working conditions, thereby affecting the accuracy of wavelength control. Predictive modeling is performed on the wavelength output data of the Micro-LED array. The historical spectral data collected by the spectral measurement unit is input into the predictor for time series analysis. Through time series analysis, the predictor can extract multi-dimensional spectral feature sequences from the historical data. These spectral feature sequences reflect the change pattern of wavelength output in the system over time and the correlation between different wavelengths. For example, assuming the obtained spectral data In time The spectral value at is represented as a vector ,in It is Wavelength in time The multidimensional spectral feature sequence is represented as a matrix, where each row corresponds to the spectral output at a moment and each column corresponds to the spectral intensity value of a wavelength. , the device-level modeling of the multi-dimensional spectral feature sequence is performed to obtain the device-level prediction model. The robustness analysis of the device-level prediction model is performed to ensure that the system can maintain stable and accurate wavelength control even in the case of external disturbances or internal changes. This analysis is achieved by establishing wavelength stability constraints. Assume that the wavelength stability constraint is set to , which indicates the maximum deviation allowed for the wavelength output within a certain time range. Assuming the wavelength output The following inequality should be satisfied: ; in is the target wavelength, is the actual wavelength at the current moment, and is the stability constraint of the wavelength. In the robustness analysis, the device-level model is simulated to evaluate whether the system meets this stability constraint under different disturbance conditions. If the system can maintain the stability of the wavelength under all possible interference conditions, the device-level prediction model can provide effective support for subsequent system-level modeling. According to the working status of the spectral measurement unit, an event trigger mechanism is constructed. The event trigger mechanism is used to ensure that the system can respond in time when abnormal fluctuations occur, and the update of the control strategy is triggered by setting a threshold. This mechanism helps the system to make real-time adjustments when the working state changes. In order to optimize the responsiveness of the event trigger mechanism, an enhanced trigger strategy is adopted. The enhanced trigger strategy introduces a reinforcement learning method to continuously optimize the trigger timing according to historical data to ensure timely and effective adjustments when the wavelength output deviates. The mathematical model of the enhanced trigger strategy is expressed as: ; in, It's in time Control actions taken at all times, It's time Instant rewards at any time, is the discount factor, It is for the future moment Estimation of state value. Through continuous iterative updates, the enhanced trigger strategy can achieve optimal wavelength control. Based on the enhanced trigger strategy, the signal transmission characteristics of the spectrum measurement unit are elastically analyzed to evaluate the system's recovery ability under various disturbances. By analyzing the elastic coefficient of the signal transmission process , calculate the compensation required when the wavelength output of the system changes , and obtain the corresponding elastic control compensation parameters. Assuming the elastic coefficient Defined as the time ratio required for the system to recover to a stable state under disturbance conditions, the control compensation parameter It is calculated according to the following formula: ; in is the initial wavelength value, is the coefficient of elastic recovery, It is the wavelength deviation that needs to be compensated. The device-level prediction model, system-level wavelength regulation model and elastic control compensation parameters are combined to form a complete prediction control model.

[0027] In a specific embodiment, the process of executing step 200 may specifically include the following steps: Extracting high-frequency dynamic components from the wavelength measurement data output by the predictive control model to obtain high-frequency spectral components, and inputting the high-frequency spectral components into a dynamic parameter Kalman filter to perform wavelength signal reconstruction processing to obtain wavelength dynamic components; Sampling the light intensity signal of the luminous area to obtain basic spectrum data of the luminous area, and inputting the basic spectrum data of the luminous area into the low-frequency signal processing unit to reconstruct the static characteristics to obtain the wavelength static component; Perform bandwidth characteristic analysis on the dynamic component of the wavelength to obtain a dynamic bandwidth compensation coefficient, and perform bandwidth compensation on the static component of the wavelength according to the dynamic bandwidth compensation coefficient to obtain a compensated static component of the wavelength; The dynamic component of the wavelength and the compensated static component of the wavelength are fused in the time domain to obtain fused wavelength data, and the fused wavelength data is input into the signal calibration unit to calibrate the wavelength accuracy to obtain the target wavelength signal.

[0028] Specifically, high-frequency spectral components are extracted from the wavelength measurement data output by the predictive control model through high-frequency dynamic component extraction technology. High-frequency dynamic components represent the parts of the signal that change rapidly over time. These components are affected by external noise or rapid fluctuations of the system. Use high-frequency filtering methods, such as bandpass filters, or Fourier transform methods. Assume that the measured wavelength signal is , the high-frequency components of the signal are extracted using the following formula: ; in represents the high frequency filtering operation, It is a high-frequency dynamic component extracted from the wavelength signal. These high-frequency components are caused by rapidly changing factors in the system and are often used for subsequent dynamic filtering and noise removal. The high-frequency spectral components are input into the dynamic parameter Kalman filter to reconstruct the wavelength signal. Kalman filtering is a recursive optimal estimation method that can estimate the state of the system in a noisy background. The Kalman filter reconstructs the wavelength signal based on the high-frequency spectral components to obtain a more accurate wavelength dynamic component. The mathematical model of Kalman filtering is expressed as: ; in, represents the estimated value of the wavelength signal, is the current measured value, is the Kalman gain, is the measurement matrix. Through the recursive processing of the Kalman filter, the high-frequency dynamic component is smoothed and compensated, the noise is eliminated and the accurate wavelength dynamic component is obtained. The light intensity signal of the luminous area is sampled to obtain basic spectral data. The basic spectral data of the luminous area represents the static spectral output of the entire array without disturbance. Assume that the basic spectral signal of the luminous area is , these data come from the regular sampling of the spectral measurement unit. After obtaining the basic spectral data, the low-frequency signal processing unit is used to reconstruct the static features and extract the low-frequency part of the wavelength signal, which represents the wavelength component of the long-term stable output of the system. For low-frequency signal processing, a low-pass filter is used, or the low-frequency component is extracted through Fourier transform. The low-frequency reconstruction process is expressed as: ; in represents the low-frequency filtering operation, These low-frequency components represent the wavelength output of the system under stable working conditions. Perform bandwidth characteristic analysis to evaluate the frequency response of the signal in the system, especially under dynamic wavelength control, how to adjust the bandwidth of each signal component according to the signal bandwidth to reduce the system wavelength deviation. Assume that the frequency response of the wavelength signal is extracted from the dynamic component By analyzing the response, the dynamic bandwidth compensation coefficient is calculated , in order to compensate for the wavelength error caused by bandwidth limitation. Dynamic bandwidth compensation coefficient Calculated according to the following formula: ; in, is the maximum bandwidth allowed by the system, is the bandwidth of the dynamic component of the wavelength at the current moment. Multiply by the compensation factor , and the compensated wavelength static component is obtained : ; The compensated static component can effectively correct the wavelength deviation caused by insufficient bandwidth, ensuring the stability and accuracy of the system during dynamic wavelength control. The dynamic component of the wavelength and the compensated static component of the wavelength are fused in the time domain to obtain a comprehensive wavelength output, which combines the characteristics of dynamic control and static stability. The signal time domain fusion is weighted averaged to obtain the fused wavelength data. : ; in, and are the weight coefficients of the dynamic component and the static compensation component, respectively, and The weight coefficient is adjusted according to the needs of the system. Usually, the dynamic component has a higher weight to cope with the rapid changes of the system. The fused wavelength data is input into the signal calibration unit for wavelength accuracy calibration to ensure that the error between the fused wavelength signal and the preset target value is minimized. The calibration process calculates the calibration factor by comparing the difference between the actual output wavelength and the target wavelength. , and apply it to the correction of the wavelength signal: ; in, is the error correction term obtained through precision calibration, and finally the target wavelength signal is obtained .

[0029] In a specific embodiment, the process of executing step 300 may specifically include the following steps: Performing wavelength control time scale analysis on the target wavelength signal to obtain a wavelength time series characteristic sequence, and inputting the wavelength time series characteristic sequence into a luminous intensity analysis unit to perform luminous intensity time scale analysis to obtain a luminous intensity time series characteristic sequence; Conduct cross-time scale coupling analysis on the wavelength time series characteristic sequence and the luminous intensity time series characteristic sequence to obtain the time scale coupling characteristics; The time scale coupling characteristics are input into the driving circuit equivalent modeling unit to obtain the driving circuit equivalent network structure, and the dynamic characteristics of the control network are extracted based on the driving circuit equivalent network structure to obtain the control network equivalent network structure; Perform dynamic coupling analysis on the equivalent network structure of the drive circuit and the equivalent network structure of the control network to obtain the equivalent parameter coupling relationship; The equivalent parameter coupling relationship is input into the integrated circuit modeling unit to obtain an integrated equivalent circuit structure, and the integrated equivalent circuit structure is parameter calibrated to obtain a wavelength control equivalent circuit model.

[0030] Specifically, the target wavelength signal is subjected to a wavelength control time scale analysis. It is a signal affected by multiple factors and contains components of multiple time scales. In order to effectively analyze these components, the time series feature sequence of the wavelength signal is extracted. , which is represented by the law of wavelength signal changing over time. In order to extract these time series features, the spectrum components of the wavelength signal are analyzed by wavelet transform or Fourier transform. Assuming that the wavelength signal Decomposed into high and low frequency parts: ; in, and Respectively represent the high-frequency and low-frequency components of the wavelength signal. Through time series analysis, the wavelength time series characteristic sequence is obtained , characterizes the change of wavelength signal at different time scales. Input to the luminous intensity analysis unit to perform time scale analysis of luminous intensity. Luminous intensity signal It is closely related to the wavelength signal. The change of the wavelength signal will directly affect the luminous intensity. Assuming that the luminous intensity signal is divided into low-frequency components and high-frequency components, the luminous intensity signal is extracted by a similar method to obtain the luminous intensity time series feature sequence , the formula is: ; in, and Respectively represent the high-frequency and low-frequency components of the luminous intensity, It is the time series characteristic sequence of the luminous intensity signal, describing the law of light intensity changing with time. and the luminous intensity time series characteristic sequence Perform cross-time scale coupling analysis to analyze the interaction and coupling relationship between wavelength signals and luminous intensity signals on multiple time scales. Calculate the cross-time scale correlation between wavelength and luminous intensity signals. Assumptions It represents the coupling strength between wavelength and luminous intensity signals. The coupling characteristics are expressed by the following formula: ; in, It is the coupling characteristic of wavelength and luminous intensity signal on the time scale, which can reflect the dependence between the two. Through this analysis, the cross-time scale coupling characteristics are obtained. The coupling characteristics are input into the driving circuit equivalent modeling unit to obtain the equivalent network structure of the driving circuit. In the Micro-LED array, the driving circuit is the core part of controlling the light source, which consists of a set of current sources and resistance elements. In order to realize the dynamic response of the wavelength control driving circuit, the coupling between wavelength and luminous intensity is considered. By modeling the driving circuit based on the coupling characteristics, a simplified circuit model is obtained. It is expressed as: ; in, is the voltage of the driving circuit, is the current, is the resistor, is the inductor. According to the equivalent network structure, the dynamic characteristics of the driving circuit are extracted and its control strategy is optimized. After obtaining the equivalent network structure of the driving circuit, the dynamic characteristics of the control network are extracted to obtain the equivalent network structure of the control network. The control network is responsible for adjusting the current of the driving circuit to ensure that the brightness and wavelength of each pixel meet the design requirements. The control network is modeled using control theory methods, such as PID controller or fuzzy controller. Assume that the input signal of the control network is , the output signal is , the dynamic response of the control network is expressed by the following formula: ; in, is the transfer function of the control network, is the input signal, is the output signal. By analyzing the dynamic characteristics of the control network, the equivalent network structure of the control network is obtained. Dynamic coupling analysis is performed on the equivalent network structure of the drive circuit and the control network to analyze the interaction between the drive circuit and the control network, especially how they jointly affect the wavelength regulation. By analyzing these two networks, the equivalent parameter coupling relationship is obtained, which is expressed as: ; in, is the change in the output signal, is the change of the input signal, is a coupling matrix, which indicates the coupling strength between the driving circuit and the control network. This coupling relationship reflects the mutual influence between the two and helps to determine the optimal parameters for wavelength control. The equivalent parameter coupling relationship is input into the integrated circuit modeling unit to obtain the integrated equivalent circuit structure. In the integrated circuit, the parameters of the driving circuit and the control network need to be further optimized and calibrated to achieve the wavelength control effect. By calibrating the parameters of the integrated circuit structure, the final wavelength control equivalent circuit model is obtained.

[0031] In a specific embodiment, the process of executing step 400 may specifically include the following steps: Perform multi-time scale dynamic analysis on the wavelength control equivalent circuit model to obtain the wavelength control dynamic response data; The wavelength control dynamic response data is input into the light pattern analysis unit to extract the time series features to obtain the light pattern sequence, and the wavelength control time scale is decomposed based on the light pattern sequence to obtain the wavelength control modal component; The wavelength control modal component is input into the spectrum control analysis unit to perform luminous intensity control analysis to obtain a cross-time scale correlation matrix, and a principal component analysis is performed on the cross-time scale correlation matrix to obtain a dominant mode; The dominant mode is input into the coupling analysis unit for time scale coupling analysis to obtain coupling branch characteristics; The dynamic characteristics of the coupling branch characteristics are analyzed to obtain the inter-modal transfer function, and the inter-modal transfer function is input into the correlation analysis unit to calculate the coupling strength to obtain the modal coupling relationship.

[0032] Specifically, a multi-time scale dynamic analysis is performed on the wavelength control equivalent circuit model, and the dynamic response of the circuit system is modeled to understand its behavior at different time scales. The wavelength control equivalent circuit model consists of multiple circuit elements (such as capacitors, inductors, resistors, etc.), which together determine the dynamic response characteristics of the circuit system. Through multi-time scale analysis, we can understand the high-speed and low-speed changes in the wavelength control process, and how these changes affect the wavelength adjustment accuracy and response speed. Assume that the input signal of the wavelength control equivalent circuit model is , the output signal is By solving the differential equation of the circuit, the wavelength control dynamic response data is obtained ,For example: ; in, is the inductor, is the resistor, is the capacitor, is the current, is the voltage. Wavelength control dynamic response data By solving the differential equation, we can get the circuit system's response to the input signal. The wavelength controls the dynamic response data Input the luminescence pattern analysis unit to extract the timing features, extract the effective timing features from the wavelength control response, and reveal the changes in the luminescence pattern during the wavelength regulation process. Through the timing feature extraction, the luminescence pattern sequence is obtained. , which is a time series of wavelength control response, indicating the change pattern of wavelength at different time points. For example, assuming that the wavelength control signal experiences strong oscillations within a certain time interval, the light emission pattern sequence is extracted through Fourier transform or wavelet transform to extract the time-frequency characteristics. Assume that the light emission pattern sequence is: ; in, is the amplitude, is the frequency, is the phase, is the number of frequency terms. Lighting pattern sequence Contains the frequency characteristics of the wavelength control signal. Based on the light emission pattern sequence , decompose the wavelength control time scale to obtain the wavelength control modal component. The wavelength control modal component is the decomposition result of the different time scale characteristics in the wavelength control signal. For example, through wavelet transform or Fourier transform, the wavelength control signal is decomposed into low-frequency and high-frequency components, corresponding to the fluctuations of long time scale and short time scale, respectively. Assume that the wavelength control signal is expressed as the sum of multiple modal components: ; in, is the modal amplitude, is the modal frequency, is the modal phase, is the mode number. By decomposing, we can obtain the wavelength-controlled mode component , where each modal component represents the response of the wavelength control signal at different time scales. The wavelength control modal component is input into the spectrum control analysis unit for luminous intensity control analysis. The relationship between the wavelength control signal and the luminous intensity is studied, and their coupling at different time scales is analyzed. Assuming the luminous intensity and wavelength control mode There is some form of coupling relationship, which can be quantified by calculating correlation or mutual information. For example, the correlation matrix across time scales is Calculated by the following formula: ; Through this formula, we can get the cross-time scale correlation matrix , which represents the correlation between the wavelength control mode and the luminous intensity. The correlation matrix is ​​processed by principal component analysis to obtain the dominant mode, which is the component with the strongest coupling between wavelength control and luminous intensity. The main components are found by eigenvalue decomposition of the covariance matrix. Assume that the correlation matrix The covariance matrix of , then the principal component Calculated by the following formula: ; in, is the eigenvalue, is the eigenvector, i.e., the principal component. The dominant modes are extracted through these principal components. The dominant modes are input into the coupling analysis unit for time scale coupling analysis to determine the mutual influence and coupling relationship between different modes. Through the coupling analysis of the dominant modes, the coupling branch characteristics are obtained, which represent the intensity and form of the mutual influence between different modes. For example, using the transfer function Describe the coupling relationship between modes, transfer function It is expressed as: ; in, is the Laplace transform of the output signal, is the Laplace transform of the input signal. By analyzing the transfer function, the coupling strength between modes is obtained. The dynamic characteristics of the coupling branch characteristics are analyzed to obtain the inter-modal transfer function, and the transfer function is input into the correlation analysis unit to calculate the coupling strength. By calculating the coupling strength, the degree of mutual influence between different modes is quantitatively described, providing a basis for optimizing wavelength control. The coupling strength is obtained by calculating the amplitude of the transfer function. Assuming that the amplitude of the transfer function is , then the coupling strength It is expressed as: ; The modal coupling relationship is obtained.

[0033] In a specific embodiment, the process of executing step 500 may specifically include the following steps: Decompose the dominant mode dynamically to obtain the wavelength control parameter sequence and the luminous intensity parameter sequence; Inputting the wavelength control parameter sequence and the luminous intensity parameter sequence into the system-level robustness analysis unit to obtain robustness constraint conditions; Based on the modal coupling relationship, the robustness constraint conditions are processed by parameter classification to obtain a multi-objective optimization interval, and the multi-objective optimization interval is input into the optimization algorithm unit for iterative solution to obtain an initial control parameter matrix; Performing spectral measurement unit state monitoring on the initial control parameter matrix to obtain state diagnosis characteristics, and inputting the state diagnosis characteristics into the fault prediction unit to calculate self-recovery parameters to obtain self-recovery mechanism parameters; The control strategy is integrated with the initial control parameter matrix and the self-recovery mechanism parameters to obtain the integrated control strategy, and the integrated control strategy is input into the wavelength control unit for parameter calibration to obtain the wavelength control strategy.

[0034] Specifically, the dominant mode is dynamically decomposed to extract the wavelength control parameters and luminous intensity parameter sequences. The dominant mode represents the most influential dynamic mode in the system and determines the main response characteristics of the system. The characteristics of the dominant mode are identified by frequency domain analysis or time domain response analysis of the system. Assume that the wavelength control signal of the system is expressed as , the luminous intensity signal is expressed as , decomposing the system response into a series of modal components, specific mode It is expressed in the following form: ; in, is the amplitude of the mode, is the modal frequency, is the phase of the mode, is the mode index. By decomposing the dynamic characteristics of the dominant mode, the wavelength control parameter sequence is obtained and the luminous intensity parameter sequence , these two parameter sequences reveal the time evolution relationship between wavelength regulation and luminous intensity. The wavelength control parameter sequence and the luminous intensity parameter sequence are input into the system-level robustness analysis unit for robustness analysis to evaluate the stability and anti-interference ability of the system under different operating conditions. In this analysis, a mathematical model of the system is established, and the robustness constraints are determined by evaluating the sensitivity of the parameters. Assuming that the output of the system is With input The relationship between is described by a linear system, whose state space equation is: ; ; in, is the system state vector, is the input vector, is the output, are the state matrix, input matrix, output matrix and transfer matrix of the system. In robustness analysis, by analyzing the frequency response, gain margin, phase margin and other indicators of the system, the robustness constraints are obtained, for example: ; in, is the frequency response function of the system, is the set robustness boundary. When the robustness constraints are identified, these constraints are input into the system-level modeling unit to help the system optimize the control strategy. Based on the modal coupling relationship, the robustness constraints are parameterized and the multi-objective optimization interval is obtained. The modal coupling relationship represents the mutual influence between different modes and is described by the coupling matrix. Assume that the modal coupling matrix is , whose elements Representing modality and modal The coupling strength between them, the elements of the matrix are calculated by the following formula: ; By analyzing the modal coupling matrix, the correlation between the modes is identified, and different optimization objectives are assigned to each mode. For example, for some key modes, the optimization goal is to maximize their response amplitude; for other modes, the coupling strength is minimized. Based on these constraints, multi-objective optimization intervals are obtained, which define the optimization range of each modal parameter. The multi-objective optimization intervals are input into the optimization algorithm unit for iterative solution to obtain the initial control parameter matrix. The optimization algorithm unit uses a variety of optimization methods, such as particle swarm optimization, genetic algorithm or convex optimization method. For example, assuming the objective function is the performance index of the system. The optimization objectives include minimizing the wavelength control error and the luminous intensity error. The optimization problem is expressed as: ; in, and is the desired wavelength control signal and luminous intensity signal, is the weight coefficient, is the modal number. Through iterative solution, the initial control parameter matrix is ​​obtained , which contains the optimal parameters for wavelength control. The initial control parameter matrix is ​​used to monitor the status of the spectral measurement unit to ensure that the control system can work properly in actual applications. The spectral measurement unit provides system status diagnostic features by real-time monitoring of the output signal. These features are obtained through spectral analysis or spectrum analysis methods. Assume that the spectral signal obtained by Fourier transform is , the state diagnostic feature is expressed as: ; By analyzing the spectral signal, it is determined whether the system has any faults, such as wavelength drift, abnormal luminous intensity, etc. The state diagnosis characteristics are input into the fault prediction unit to calculate the self-recovery parameters. The control strategy is automatically adjusted by the abnormal characteristics detected to restore the system to normal working state. The self-recovery mechanism includes a fault diagnosis model, which predicts the type of fault that occurs by analyzing the state characteristics and calculates the required self-recovery parameters. Assume that the goal of the self-recovery mechanism is to adjust the wavelength control parameters and luminous intensity parameters The initial control parameter matrix and the self-recovery mechanism parameters are integrated to obtain a fusion control strategy. Conventional control and self-recovery mechanisms are combined to enable the system to recover quickly when a failure occurs. The fusion control strategy is implemented through the weighted average method or fuzzy control algorithm. The final control strategy It is expressed as: ; in, is a weight factor, which indicates the weight ratio of conventional control and self-recovery control. Through this fusion control strategy, the stability and robustness of the wavelength control system under different environments and operating conditions are ensured. The fusion control strategy is input into the wavelength control unit for parameter calibration to obtain the final wavelength control strategy.

[0035] In a specific embodiment, the process of executing step 600 may specifically include the following steps: Analyze the parameters of the wavelength control strategy to obtain the operating parameters of the control system, and input the wavelength signal collected by the spectrum measurement unit into the bandwidth extension module for signal enhancement to obtain the processed wavelength signal; Based on the operating parameters of the control system, a difference operation is performed on the processed wavelength signal and the preset target value to obtain wavelength deviation data, and the wavelength deviation data is input into the control quantity calculation unit for strategy matching to obtain the initial driving control quantity; The working state of the spectrum measurement unit is monitored in real time to obtain the operating state characteristics, and the initial driving control amount is compensated based on the operating state characteristics to obtain the compensated driving control amount; The compensated drive control quantity is input into the fault diagnosis unit for stability analysis to obtain the target drive control quantity, and the target drive control quantity is matched with the drive circuit and output to the Micro-LED drive circuit.

[0036] Specifically, the wavelength regulation control strategy is parameter parsed to extract the operating parameters of the control system. Assume that the wavelength regulation control strategy is described by the following formula: ; in, is a set of control strategy parameters, is the weight coefficient of each control parameter, is the number of control parameters. By analyzing the various parameters of the control strategy, the operating parameters required by the system are obtained, such as the gain value and response time of each wavelength control component. These parameters are applied to the entire system model to ensure that the system achieves the expected control effect in actual application. The wavelength signal collected by the spectral measurement unit is input into the bandwidth extension module for signal enhancement. The wavelength signal obtained by the spectral measurement unit is the real-time spectral data transmitted in the system, which is processed by bandwidth expansion to improve the responsiveness and accuracy of the system. The collected signal is analyzed in the frequency domain. Assuming that the measured wavelength signal is , the output signal of the bandwidth extension module is obtained by the following bandwidth extension formula: ; in, is the gain function of the bandwidth extension module, which represents the expansion characteristics of the signal in the frequency domain. is the frequency of the signal. Through this processing, the bandwidth of the signal is enhanced and can be precisely controlled within a wider frequency band, thereby improving the dynamic response capability and sensitivity of the system. Based on the operating parameters of the control system, the difference between the processed wavelength signal and the preset target value is calculated to obtain the wavelength deviation data. The system output is evaluated and the error between the actual wavelength and the expected target is calculated. Assume that the preset target wavelength is , then the wavelength deviation data It is expressed as: ; in, is the wavelength deviation data, which indicates the difference between the actual output and the target. By calculating the wavelength deviation data in real time, the effectiveness of the current control strategy is reflected, and optimization is performed based on these deviations. The wavelength deviation data is input into the control quantity calculation unit for strategy matching to obtain the initial drive control quantity. The control quantity calculation unit analyzes the wavelength deviation data according to the preset adjustment strategy, and uses a suitable control algorithm (such as PID controller, fuzzy controller, etc.) for feedback adjustment to generate the initial drive control quantity. Assume that the initial drive control quantity is , then its calculation formula is expressed as: ; in, They are proportional, integral and differential gain coefficients, which determine the speed and accuracy of the system response. Through these adjustment parameters, the control quantity calculation unit generates appropriate control signals according to the wavelength deviation data, thereby adjusting the behavior of the drive circuit. In order to optimize the control effect of the system, the working state of the spectrum measurement unit is monitored in real time to obtain the operating state characteristics. The operating state characteristics are obtained through real-time analysis of the spectrum data, including parameters such as the frequency characteristics of the signal, amplitude changes, and noise levels. Assume that the state characteristics obtained by real-time monitoring are These features are used to determine whether the system is abnormal or has performance degradation. For example, the diagnosis of system status is based on spectrum analysis. Assume that the spectrum distribution of the status feature is , the formula is: ; Based on the real-time monitoring results, the stability of the system operation and the accuracy of wavelength control are judged. If an abnormality is detected (such as a shift in the spectral signal or an increase in noise), the initial drive control amount needs to be compensated. The compensation process adjusts the initial drive control amount according to the operating status characteristics to ensure that the system can return to normal working state when an abnormality occurs. The compensated drive control amount It is expressed by the following formula: ; in, It is a compensation amount based on state characteristics and is calculated by an adaptive control algorithm. The size of the compensation amount depends on the difference between the real-time monitoring result and the expected state. For example, if the spectral signal deviates a lot from the expected target, the compensation amount will increase accordingly. The compensated drive control amount is input into the fault diagnosis unit for stability analysis to obtain the target drive control amount. The fault diagnosis unit performs stability analysis on the compensated control signal to ensure that the control amount can remain stable in actual application and will not cause oscillation or over-adjustment of the system. Stability analysis includes techniques such as pole analysis and root locus analysis of the system. If the system is found to be unstable through analysis, the compensation strategy or control algorithm will be readjusted. Assume that the target drive control amount is , the stability analysis is achieved through the following formula: ; in, is the pole function in the stability analysis, and the output is the stability judgment result of the system. The target drive control quantity is input into the drive circuit matching unit to ensure the matching between the output drive signal and the target wavelength signal. Assume that the signal output to the drive circuit is , then the drive circuit matching is achieved by the following formula: ; Through these steps, the wavelength regulation control strategy can be adjusted and compensated in real time to ensure the stable operation of the Micro-LED driving circuit and accurately control the wavelength output of each pixel, thereby ensuring that the performance of the entire display device reaches the optimal state.

[0037] The above describes the wavelength control method of the ultra-high pixel density Micro-LED micro display device in the embodiment of the present application. The following describes the wavelength control device 10 of the ultra-high pixel density Micro-LED micro display device in the embodiment of the present application. Figure 2 In the embodiment of the present application, an embodiment of the wavelength control device 10 of the ultra-high pixel density Micro-LED micro display device includes: A hierarchical modeling module 11 is used to perform hierarchical modeling on the device-level characteristics and system-level characteristics of the Micro-LED array to obtain a predictive control model; A fusion module 12 is used to perform static bandwidth expansion processing on the spectral signal and the luminous area according to the predictive control model to obtain a wavelength dynamic component and a wavelength static component, and fuse the wavelength dynamic component with the wavelength static component to obtain a target wavelength signal; An equivalent circuit modeling module 13 is used to perform equivalent circuit modeling on the Micro-LED driving circuit and the control network based on the target wavelength signal to obtain a wavelength control equivalent circuit model; A modal decomposition module 14 is used to perform modal decomposition processing on the wavelength control equivalent circuit model to obtain the dominant mode and modal coupling relationship; An optimization module 15 is used to perform multi-objective parameter optimization based on the dominant mode and the mode coupling relationship to obtain a wavelength regulation control strategy; The control module 16 is used to input the wavelength regulation control strategy into the control system for closed-loop control, compare the collected spectral measurement signal with the preset target value, calculate the deviation value and output the control amount to the Micro-LED driving circuit.

[0038] Through the collaboration of the above components and the construction of a hierarchical predictive control model, the collaborative modeling of device-level and system-level characteristics is achieved, the crosstalk effect problem under high pixel density conditions is effectively solved, and the accuracy of wavelength control is improved. The static bandwidth extension technology is used to successfully achieve the accurate extraction and fusion of the dynamic and static components of the wavelength, overcome the bandwidth limitation of the traditional single spectral sensor, and improve the wavelength measurement accuracy. An equivalent circuit model for wavelength control is established. Through multi-time scale dynamic analysis, the cross-time scale coupling mechanism of wavelength control and luminous intensity is revealed. The modal decomposition technology is used to accurately identify the dominant mode and modal coupling relationship, achieve an accurate description of the multi-time scale dynamic characteristics, and improve the pertinence of the control strategy. The introduction of a multi-objective parameter optimization method combined with a self-recovery mechanism significantly improves the robustness and reliability of wavelength control, and effectively copes with the failure of the spectral measurement unit and the communication interruption problem. Through the implementation of a closed-loop control strategy, real-time and accurate control of wavelength control is achieved, ensuring the stable operation and display quality of the ultra-high pixel density Micro-LED micro-display device.

[0039] See also Figure 3 , Figure 3 This is a schematic block diagram of the structure of an electronic device 300 provided in an embodiment of the present application. The electronic device 300 includes a processor 301 and a memory 302. The processor 301 and the memory 302 are connected via a device bus 303, wherein the memory 302 may include a non-volatile storage medium and an internal memory.

[0040] The non-volatile storage medium can store a computer program. The computer program includes program instructions, and when the program instructions are executed by the processor 301, the processor 301 can execute any of the above-mentioned ultra-high pixel density Micro-LED micro-display device wavelength control methods.

[0041] The processor 301 is used to provide computing and control capabilities to support the operation of the entire electronic device 300 .

[0042] The internal memory provides an environment for the operation of the computer program in the non-volatile storage medium. When the computer program is executed by the processor 301, the processor 301 can execute any of the above-mentioned ultra-high pixel density Micro-LED micro display device wavelength control methods.

[0043] Those skilled in the art will understand that Figure 3 The structure shown in the figure is only a block diagram of a partial structure related to the present application scheme, and does not constitute a limitation on the electronic device 300 involved in the present application scheme. The specific electronic device 300 may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0044] It should be understood that the processor 301 may be a central processing unit (CPU), and the processor 301 may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0045] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the electronic device 300 described above can refer to the corresponding process of the aforementioned ultra-high pixel density Micro-LED micro display device wavelength control method, and will not be repeated here.

[0046] An embodiment of the present application also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by one or more processors, the one or more processors implement the wavelength control method of the ultra-high pixel density Micro-LED micro display device provided in the embodiment of the present application.

[0047] The computer-readable storage medium may be an internal storage unit of the electronic device 300 in the aforementioned embodiment, such as a hard disk or memory of the electronic device 300. The computer-readable storage medium may also be an external storage device of the electronic device 300, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped with the electronic device 300.

[0048] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0049] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable an electronic device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk, etc., various media that can store program codes.

[0050] As described above, the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A wavelength control method for an ultra-high pixel density Micro-LED micro-display device, characterized in that: The method comprises: The device-level characteristics and system-level characteristics of the Micro-LED array are hierarchically modeled to obtain a predictive control model; Performing static bandwidth expansion processing on the spectral signal and the luminous area according to the predictive control model to obtain a wavelength dynamic component and a wavelength static component, and fusing the wavelength dynamic component with the wavelength static component to obtain a target wavelength signal; Performing equivalent circuit modeling on the Micro-LED driving circuit and control network based on the target wavelength signal to obtain a wavelength control equivalent circuit model; Performing modal decomposition processing on the wavelength control equivalent circuit model to obtain dominant modes and modal coupling relationships; Performing multi-objective parameter optimization based on the dominant mode and the mode coupling relationship to obtain a wavelength regulation control strategy; The wavelength regulation control strategy is input into the control system for closed-loop control, the collected spectral measurement signal is compared with the preset target value, the deviation value is calculated and the control amount is output to the Micro-LED driving circuit.

2. The wavelength control method of the ultra-high pixel density Micro-LED micro-display device according to claim 1, characterized in that: The device-level characteristics and system-level characteristics of the Micro-LED array are hierarchically modeled to obtain a predictive control model, including: Performing crosstalk effect analysis on wavelength output data in the Micro-LED array to obtain an inter-pixel crosstalk matrix, wherein the inter-pixel crosstalk matrix represents the degree of wavelength interference between adjacent pixels; Inputting historical spectral data collected by the spectral measurement unit into a predictor for time series analysis to obtain a multi-dimensional spectral feature sequence, and performing device-level modeling on the multi-dimensional spectral feature sequence based on the inter-pixel crosstalk matrix to obtain a device-level prediction model; Performing robustness analysis on the device-level prediction model to obtain wavelength stability constraints, and inputting the wavelength stability constraints into a system-level modeling unit to obtain a system-level wavelength control model; An event trigger mechanism is constructed according to the working state of the spectrum measurement unit to obtain an enhanced trigger strategy, and a signal transmission characteristic of the spectrum measurement unit is elastically analyzed based on the enhanced trigger strategy to obtain an elastic control compensation parameter; The device-level prediction model, the system-level wavelength regulation model and the elastic control compensation parameter are integrated to obtain a prediction control model.

3. The wavelength control method of the ultra-high pixel density Micro-LED micro-display device according to claim 2, characterized in that: The method of performing static bandwidth expansion processing on the spectral signal and the luminous area according to the predictive control model to obtain a wavelength dynamic component and a wavelength static component, and fusing the wavelength dynamic component with the wavelength static component to obtain a target wavelength signal includes: Extracting high-frequency dynamic components from the wavelength measurement data output by the predictive control model to obtain high-frequency spectral components, and inputting the high-frequency spectral components into a dynamic parameter Kalman filter to perform wavelength signal reconstruction processing to obtain wavelength dynamic components; Sampling the light intensity signal of the luminous area to obtain basic spectrum data of the luminous area, and inputting the basic spectrum data of the luminous area into a low-frequency signal processing unit to reconstruct static features to obtain a wavelength static component; Performing bandwidth characteristic analysis on the dynamic component of the wavelength to obtain a dynamic bandwidth compensation coefficient, and performing bandwidth compensation on the static component of the wavelength according to the dynamic bandwidth compensation coefficient to obtain a compensated static component of the wavelength; The dynamic wavelength component and the compensated static wavelength component are fused in the time domain to obtain fused wavelength data, and the fused wavelength data is input into a signal calibration unit to perform wavelength accuracy calibration to obtain a target wavelength signal.

4. The wavelength control method of the ultra-high pixel density Micro-LED micro-display device according to claim 3, characterized in that: The performing equivalent circuit modeling on the Micro-LED driving circuit and the control network based on the target wavelength signal to obtain a wavelength control equivalent circuit model includes: Performing wavelength control time scale analysis on the target wavelength signal to obtain a wavelength time series characteristic sequence, and inputting the wavelength time series characteristic sequence into a luminous intensity analysis unit to perform luminous intensity time scale analysis to obtain a luminous intensity time series characteristic sequence; Performing cross-time scale coupling analysis on the wavelength time series characteristic sequence and the luminous intensity time series characteristic sequence to obtain time scale coupling characteristics; Inputting the time scale coupling characteristics into a driving circuit equivalent modeling unit to obtain a driving circuit equivalent network structure, and extracting dynamic characteristics of a control network based on the driving circuit equivalent network structure to obtain a control network equivalent network structure; Performing dynamic coupling analysis on the equivalent network structure of the driving circuit and the equivalent network structure of the control network to obtain an equivalent parameter coupling relationship; The equivalent parameter coupling relationship is input into an integrated circuit modeling unit to obtain an integrated equivalent circuit structure, and parameter calibration is performed on the integrated equivalent circuit structure to obtain a wavelength control equivalent circuit model.

5. The wavelength control method of the ultra-high pixel density Micro-LED micro-display device according to claim 4, characterized in that: The modal decomposition processing is performed on the wavelength control equivalent circuit model to obtain the dominant mode and the modal coupling relationship, including: Performing multi-time scale dynamic analysis on the wavelength control equivalent circuit model to obtain wavelength control dynamic response data; Inputting the wavelength control dynamic response data into a light pattern analysis unit to extract time series features to obtain a light pattern sequence, and decomposing the wavelength control time scale based on the light pattern sequence to obtain a wavelength control modal component; Inputting the wavelength control modal component into the spectrum control analysis unit to perform luminous intensity control analysis to obtain a cross-time scale correlation matrix, and performing principal component analysis on the cross-time scale correlation matrix to obtain a dominant mode; Performing time scale coupling analysis on the dominant mode input coupling analysis unit to obtain coupling branch characteristics; The coupling branch characteristics are subjected to dynamic characteristic analysis to obtain an inter-modal transfer function, and the inter-modal transfer function is input into a correlation analysis unit to calculate coupling strength to obtain a modal coupling relationship.

6. The wavelength control method of the ultra-high pixel density Micro-LED micro-display device according to claim 5, characterized in that: The multi-objective parameter optimization is performed based on the dominant mode and the mode coupling relationship to obtain a wavelength regulation control strategy, including: Decomposing the dominant mode dynamically to obtain a wavelength control parameter sequence and a luminous intensity parameter sequence; Inputting the wavelength control parameter sequence and the luminous intensity parameter sequence into a system-level robustness analysis unit to obtain robustness constraint conditions; Based on the modal coupling relationship, the robustness constraint condition is subjected to parameter classification processing to obtain a multi-objective optimization interval, and the multi-objective optimization interval is input into an optimization algorithm unit for iterative solution to obtain an initial control parameter matrix; Performing spectrum measurement unit state monitoring on the initial control parameter matrix to obtain state diagnosis characteristics, and inputting the state diagnosis characteristics into a fault prediction unit to perform self-recovery parameter calculation to obtain self-recovery mechanism parameters; The initial control parameter matrix and the self-recovery mechanism parameters are subjected to control strategy fusion to obtain a fusion control strategy, and the fusion control strategy is input into a wavelength control unit for parameter calibration to obtain a wavelength control strategy.

7. The wavelength control method of the ultra-high pixel density Micro-LED micro-display device according to claim 6, characterized in that: The wavelength regulation control strategy is input into the control system for closed-loop control, the collected spectrum measurement signal is compared with the preset target value, the deviation value is calculated and the control amount is output to the Micro-LED driving circuit, including: Analyzing the parameters of the wavelength regulation control strategy to obtain the operating parameters of the control system, and inputting the wavelength signal collected by the spectrum measurement unit into the bandwidth extension module for signal enhancement to obtain a processed wavelength signal; Based on the operating parameters of the control system, a difference operation is performed on the processed wavelength signal and a preset target value to obtain wavelength deviation data, and the wavelength deviation data is input into a control amount calculation unit for strategy matching to obtain an initial drive control amount; Performing real-time monitoring on the working state of the spectrum measurement unit to obtain operating state characteristics, and compensating the initial driving control amount based on the operating state characteristics to obtain a compensated driving control amount; The compensated driving control amount is input into a fault diagnosis unit for stability analysis to obtain a target driving control amount, and the target driving control amount is matched with a driving circuit and output to the Micro-LED driving circuit.

8. A wavelength control device for an ultra-high pixel density Micro-LED micro-display device, characterized in that: Used to perform the wavelength control method of the ultra-high pixel density Micro-LED micro display device according to any one of claims 1 to 7, the ultra-high pixel density Micro-LED micro display device wavelength control device comprises: A hierarchical modeling module is used to perform hierarchical modeling on the device-level characteristics and system-level characteristics of the Micro-LED array to obtain a predictive control model; A fusion module, used for performing static bandwidth expansion processing on the spectral signal and the luminous area according to the predictive control model to obtain a wavelength dynamic component and a wavelength static component, and fusing the wavelength dynamic component with the wavelength static component to obtain a target wavelength signal; An equivalent circuit modeling module, used to perform equivalent circuit modeling on the Micro-LED driving circuit and the control network based on the target wavelength signal to obtain a wavelength control equivalent circuit model; A modal decomposition module, used for performing modal decomposition processing on the wavelength control equivalent circuit model to obtain dominant modes and modal coupling relationships; An optimization module, used for performing multi-objective parameter optimization based on the dominant mode and the mode coupling relationship to obtain a wavelength regulation control strategy; The control module is used to input the wavelength regulation control strategy into the control system for closed-loop control, compare the collected spectral measurement signal with the preset target value, calculate the deviation value and output the control amount to the Micro-LED driving circuit.

9. An electronic device, characterized in that: The electronic device comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory so that the electronic device executes the wavelength control method of the ultra-high pixel density Micro-LED micro display device as described in any one of claims 1-7.

10. A computer-readable storage medium having instructions stored thereon, characterized in that: When the instruction is executed by the processor, the wavelength control method of the ultra-high pixel density Micro-LED micro display device as described in any one of claims 1-7 is implemented.

Images