An optical supplemental method suitable for high speed modulation of LEDs

Abstract

This invention discloses an optical compensation method suitable for high-speed modulated LEDs, comprising: extracting the uneven light field distribution features from the smoothed light output waveform; reconstructing the internal light field using a multi-layer permeation structure to obtain optimized light field distribution data; acquiring eddy current disturbance phase information; applying opposite phases to cancel eddy components using a phase modulation algorithm to obtain a stable light field distribution result; constructing an optical path loop feedback path to capture scattered light; injecting supplementary light after delay line processing to obtain loop light source data; identifying distortion types using a support vector machine algorithm to obtain adaptive compensation selection scheme parameters; determining the channel status of the redundant architecture based on the adaptive compensation selection scheme parameters; if the main channel fails, switching to the backup channel and allocating weights to obtain a fault-tolerant supplementary output. This invention significantly improves the output stability and transmission efficiency of high-speed modulated LEDs.

Description

Technical Field

[0001] This invention belongs to the field of optical display, and particularly relates to an optical supplementation method suitable for high-speed modulated LEDs. Background Technology

[0002] In the fields of modern communication and display technology, high-speed modulated light-emitting diodes (LEDs) have attracted much attention as a key light source technology due to their high efficiency, low power consumption, and fast response. Their importance is particularly evident in scenarios involving high-speed data transmission and dynamic light field modulation. However, with the continuous increase in modulation speed, maintaining the stability and quality of light output under high-frequency conditions has become a core challenge that urgently needs to be addressed in this field.

[0003] Current technical solutions often struggle to adapt to complex dynamic environments when facing the challenges of high-speed modulation, especially when optical output is interfered with by various internal and external factors. Existing methods tend to focus on optimizing single aspects, neglecting the comprehensive imbalance in the temporal, spatial, and energy distribution of optical signals. This imbalance not only affects signal transmission quality but can also lead to a decline in overall system performance, particularly in applications requiring prolonged high-intensity operation.

[0004] Focusing on the technical challenges, high-speed modulated light-emitting diodes (LEDs) face two major obstacles during rapid switching: light intensity fluctuations and response delays. Light intensity fluctuations stem from uneven energy distribution within the device under high-frequency modulation, leading to abrupt changes or overshoots in the output optical signal, affecting signal clarity. These fluctuations further exacerbate the response delay problem because the device's internal physical characteristics cannot instantly adapt to changes in the electrical signal, resulting in a time discrepancy between the optical output and the input command. For example, in high-speed data transmission, when the modulation frequency reaches a certain level, the waveform of the optical signal may become severely distorted, and some data points may be lost due to insufficient light intensity or delay, directly impacting the accuracy and reliability of communication.

[0005] Therefore, how to effectively suppress light intensity fluctuations under high-frequency modulation, while shortening the response delay and ensuring stable output of optical signals in terms of time and intensity, has become a key problem that urgently needs to be solved. Summary of the Invention

[0006] To address the aforementioned technical problems, this invention provides an optical compensation method suitable for high-speed modulated LEDs, comprising: The real-time modulation signal amplitude of the high-speed modulation LED is obtained, and the nonlinear attenuation characteristics exceeding the threshold are extracted. A preset threshold detection algorithm is used to determine the amplitude change trend and obtain the attenuation compensation requirement. Based on the attenuation compensation requirement, the initial parameters of the optical supplement injection are determined. Based on the light intensity fluctuation suppression requirement, the transient disturbance energy is absorbed through the optical smoothing buffer layer to obtain the smoothed light output waveform. The uneven light field distribution features are extracted from the smoothed light output waveform, and the internal light field is reconstructed using a multi-layer permeation structure to obtain optimized light field distribution data. Based on the optimized optical field distribution data, the necessity of response inertial compensation is determined. If carrier lifetime delay is detected, compensation energy is injected through the negative delay system feedforward to obtain an output signal that closely approximates the ideal waveform. Based on the output signal, the phase information of the eddy current disturbance is obtained, and the opposite phase is applied to cancel the eddy component using a phase modulation algorithm to obtain the optical field distribution result. Based on the light field distribution results, an optical path loop feedback path is constructed to capture scattered light, which is then processed by a delay line and injected to supplement the light, thus obtaining loop light source data. Based on the circulating light source data, multi-dimensional identification is performed, and the distortion type is identified using the support vector machine algorithm to obtain the parameters of the adaptive compensation selection scheme. Based on the selected adaptive compensation scheme parameters, the channel status of the redundant architecture is determined. If the main channel fails, the weight is allocated to the backup channel to obtain the supplementary output after fault tolerance. The multi-mode set is fused from the fault-tolerant supplementary output, the weights are adjusted by the parameter optimizer to obtain the optimized multi-mode supplementary signal, and the abnormal part is reconstructed by the state storage backup to obtain the final stable LED output.

[0007] Preferably, the process of obtaining the attenuation compensation requirement includes: The modulation signal of the high-speed modulation light-emitting diode is acquired, the signal amplitude data is recorded by a real-time acquisition device, and the acquired signal is initially filtered by digital signal processing technology to obtain smoothed signal amplitude data. Based on the smoothed signal amplitude data, a preset threshold is applied for preliminary screening. If the signal amplitude exceeds the preset threshold, it is marked as an anomaly, and the location and amplitude value of the anomaly are recorded to obtain the distribution result of the abnormal signal. Based on the distribution results of the abnormal signals, the characteristic data of nonlinear attenuation are extracted. By performing time-series analysis on the amplitude changes of the abnormal points, the trend direction and rate of amplitude changes are determined, and the quantitative results of the change trend are obtained. Based on the quantification results of the changing trend, analyze the correspondence between the characteristics of nonlinear decay and the amplitude change. If the changing trend shows a continuous decline, record the duration of the decline and the range of amplitude reduction to determine the severity of the decay characteristics. Based on the severity of the attenuation characteristics, the parameters of the compensation requirement are calculated, and the attenuation characteristics are matched using a preset compensation model to obtain the adjustment value of the compensation requirement. Based on the adjustment value of the compensation requirement, the driving parameters of the modulation signal are dynamically corrected, and a real-time feedback mechanism is used to monitor the amplitude of the corrected signal to determine whether the compensated signal has recovered to the expected range. Based on the monitoring results, if the compensated signal amplitude still does not reach the expected range, the parameters of the compensation model are fine-tuned, and the signal amplitude is further optimized through iterative processing to obtain the final modulated signal output.

[0008] Preferably, the process of obtaining the smoothed light output waveform includes: By acquiring data for attenuation compensation, the real-time attenuation value of the optical system is obtained, and the compensation reference value of the current system is determined. Based on the compensation reference value, the initial parameter configuration for optical injection is calculated to obtain a set of parameters suitable for the current light intensity fluctuation; Based on the parameter set, a preset optical model is used for simulation to determine whether the light intensity fluctuation is within an acceptable range. If it exceeds the preset threshold, the key values ​​in the parameter set are adjusted to obtain the optimized parameter configuration. Based on the optimized parameter configuration, control data suitable for smooth buffering is extracted to determine the energy absorption strategy for transient disturbances. According to the energy absorption strategy, the working mode of deploying the optical smoothing buffer layer is used to obtain real-time energy data of transient disturbances, determine whether the preset absorption effect has been achieved, and if not, dynamically adjust the buffer layer to obtain a stable energy control result. Based on the energy control results, smoothed light output waveform data is generated, and the smoothness of the final light output wave is determined. Based on the smoothness of the light output wave, relevant data is recorded to the system log to obtain complete operating status information and determine whether further optimization is needed.

[0009] Preferably, the process of obtaining optimized light field distribution data includes: By performing preliminary analysis on the data of the light output wave, key point information in the waveform is obtained, and initial light field distribution data is obtained. Based on the initial light field distribution data, a smoothing process is used to denoise the waveform, and the smoothed light output waveform data is determined. Based on the smoothed light output waveform data, the non-uniformity in the light field distribution is extracted to determine whether there is a significant non-uniform region. If the non-uniformity exceeds a preset threshold, the corresponding distribution characteristic data is recorded. By performing layered analysis of the internal light field data through a multi-layered permeation structure, the distribution characteristics of each layer are obtained, resulting in layered light field distribution data. The layered light field distribution data is reconstructed to adjust the distribution characteristics of the internal light field and generate preliminary optimized light field data. The preliminary optimized light field data were verified using structural analysis methods to obtain the optimized distribution characteristics and determine the final optimized light field data. The final optimized light field data is stored and formatted to generate optimized light field distribution data that can be used for subsequent analysis.

[0010] Preferably, the process of obtaining an output signal that closely approximates the ideal waveform includes: By analyzing the light field distribution data, preliminary characteristics of the response inertia can be obtained to determine whether there is an abnormal delay phenomenon. If a carrier lifetime delay is detected in the response inertia, the delay detection module is triggered to obtain the specific delay parameters and determine whether the delay exceeds the preset threshold range. Based on the delay detection results, the negative delay system is activated, the required feedforward injection parameters are calculated, and the initial configuration scheme for compensation energy is obtained. Based on the initial configuration scheme of the compensation energy, the execution strategy of feedforward injection is adjusted, the adjusted energy injection data is obtained, and the final energy compensation value is determined. The final energy compensation value is input into the signal processing module to generate signal data that approximates the ideal waveform, thereby obtaining an optimized output signal. Key features are extracted from the optimized output signal, and combined with the system optimization feedback mechanism, subsequent parameters of signal processing are adjusted to determine the final output signal.

[0011] Preferably, the process of obtaining the light field distribution result includes: Phase data of eddy current disturbance is obtained from the output signal, and key phase information is separated using signal processing techniques to obtain a preliminary phase distribution. Based on the preliminary phase distribution, a corresponding reverse phase signal is generated using phase modulation technology, and the parameter values ​​of the reverse phase are determined. If the phase deviation of the generated reverse phase signal exceeds the preset threshold, the reverse phase is recalculated by adjusting the modulation parameters to obtain the reverse phase signal. The core data for canceling vortex components is extracted from the reverse phase signal and combined with the original output signal through superposition processing to obtain preliminary optical field adjustment results; Based on the preliminary light field adjustment results, the stability index of the light field distribution is obtained. If the stability index does not meet the preset standard, the phase superposition is further optimized through iterative processing to determine the final stability data. Based on the final stability data, a stable light field distribution is generated, the vortex component is canceled out, and the target light field distribution is obtained. Based on the target light field distribution, the distribution parameters are recorded and saved to generate a light field control scheme that can be called upon later.

[0012] Preferably, the process of obtaining the circulating light source data includes: The light field distribution data is collected and stored to construct an initial light field distribution dataset. The data is then classified and organized using a pre-established analysis model to obtain structured light field distribution information. Based on the structured light field distribution information, a cyclic feedback mechanism is designed to simulate and calculate the light path, thereby obtaining the geometric layout and parameter configuration of the feedback path. Based on the geometric layout of the feedback path, a scattering capture operation is performed to obtain scattered light signal data. The captured data is then filtered to obtain a clear scattered light signal. The scattered light signal is subjected to a delay processing procedure. The delay line technology is used to adjust the time of the signal. The time offset of the signal is judged. If the offset exceeds a preset threshold, correction processing is performed to obtain the adjusted signal data. An injection and replenishment operation is performed on the adjusted signal data to re-inject the processed signal into the circulating light source system and obtain the replenished light source signal intensity information. Based on the supplemented light source signal intensity information, the light source data is optimized and classified using the support vector machine algorithm to determine the final cyclic light source data configuration. Based on the final circulating light source data configuration, complete circulating light source operating parameters are generated, and the operating parameters are stored and updated to obtain circulating light source data for subsequent light field optimization.

[0013] Preferably, the process of obtaining the parameters of the adaptive compensation selection scheme includes: Raw data is collected from a circulating light source and preprocessed to obtain a pre-cleaned dataset. Multi-dimensional feature information is extracted from the cleaned dataset, and feature selection methods are used to screen feature groups that are highly correlated with the distortion type to obtain key feature combinations. Based on the key feature combination, the support vector machine algorithm is applied to classify the distortion type. If the confidence of the classification result is lower than the preset threshold, the feature group is extracted a second time to obtain a more accurate classification result. Based on the classification results, analyze the distribution pattern of distortion types, obtain the influencing factors corresponding to the distortion types, and identify the main sources of distortion. Based on the main sources of distortion, the initial parameters for adaptive compensation are calculated, and a preliminary compensation scheme is obtained by combining the light source analysis data. The preliminary compensation scheme is compared with the efficiency improvement target. If the matching degree of the compensation scheme does not meet the preset standard, the parameters are fine-tuned to determine the final compensation parameter scheme. The cyclic light source data is processed using the aforementioned final compensation parameter scheme to obtain optimized light source output data.

[0014] Preferably, the process of obtaining the fault-tolerant supplementary output includes: By using a pre-established monitoring model, real-time operating data of each channel in the redundant architecture is obtained. By continuously collecting the status information of the main channel and the backup channel, the current working status of the channel is determined. If the main channel's operating data exceeds the preset threshold range, it is determined that the main channel is faulty, triggering the switching mechanism to transfer the running task to the backup channel, thus obtaining the initial channel switching results. Based on the performance parameters of the backup channel, the output of the backup channel is dynamically adjusted using a preset weight allocation rule to generate adjusted output data. Based on the adjusted output data, detect whether there is data missing or abnormal fluctuation. If an abnormality is detected, smooth the output data to obtain a stable output result. Based on the output after smoothing, and combined with the fault tolerance processing logic, the completeness of the supplementary output is checked to determine whether it meets the business requirements, and the final output after verification is generated. From the final output after verification, key status information is extracted, the channel status records of the redundant architecture are updated, and closed-loop data for status analysis is obtained. Based on the closed-loop data from the state analysis, the response speed of the switching mechanism is continuously optimized, generating a more efficient fault detection and fault-tolerant processing flow.

[0015] Preferably, the process of obtaining the final stable LED output includes: By integrating fault-tolerant data with supplementary data, an initial multi-modal set of data is obtained; Key signal features are extracted from the multi-mode set, and preliminary signal optimization results are obtained by using a preset weight parameter allocation method. Based on the preliminary signal optimization results, if an abnormal part is detected in the signal, the range and type of the abnormal part are determined by comparing it with the backup data in the state storage. Based on the type and scope of the abnormal part, the corresponding backup data is retrieved from the state storage, a backup reconstruction operation is performed, and the repaired signal data is obtained. Based on the repaired signal data, the weight parameters are adjusted to balance the contributions of the multi-mode set, and the final optimized signal content is determined. The optimized signal content is subjected to signal integration processing to generate a stable LED output signal; Based on the stable LED output signal, a preset verification mechanism is used to determine whether the signal meets the output standard, thus confirming the final LED output signal.

[0016] Compared with the prior art, the present invention has the following advantages and technical effects: This invention discloses a comprehensive processing method for optimizing and stabilizing the output signal of high-speed modulated light-emitting diodes (LEDs). It aims to solve multiple service scenarios involving nonlinear signal amplitude attenuation, uneven optical field distribution, eddy current disturbances, and channel failures during high-speed modulation. This invention extracts the amplitude characteristics of the modulated signal in real time, combines this with a preset threshold detection algorithm to determine the attenuation trend and compensation requirements, and uses an optical smoothing buffer layer to suppress light intensity fluctuations and optimize the optical field distribution. Simultaneously, it utilizes a phase modulation algorithm to cancel vortex components, constructs an optical path loop feedback path to improve efficiency, and employs a support vector machine algorithm to identify distortion types. Combined with a redundant architecture, it achieves fault-tolerant switching. Finally, through multi-mode signal fusion and parameter optimization, it ensures that the output signal closely approximates the ideal waveform. This invention, through the integrated application of multi-dimensional adaptive compensation and fault-tolerant mechanisms, significantly improves the output stability and transmission efficiency of high-speed modulated LEDs, making it suitable for complex optical communication scenarios.

[0017] This invention addresses the issue that in high-speed modulated LED systems, when the modulation frequency exceeds a specific threshold, the light output of the LED exhibits a nonlinear attenuation phenomenon. To address this, this invention establishes a dynamic threshold detection mechanism. By monitoring the amplitude change of the modulation signal in real time, the optical compensation system is activated when the signal approaches or exceeds the LED response threshold. A pre-distortion compensation algorithm is used to calculate the required additional light intensity injection. An auxiliary light source or optical gain medium is then used to precisely supplement the light output of the main LED, ensuring a stable light power output throughout the entire modulation cycle. The calculation of the supplementary amount needs to consider the LED's thermal effect, carrier recombination rate, and the frequency-dependent characteristics of quantum efficiency.

[0018] This invention addresses the issue of light intensity fluctuations and overshoot that occur during the rapid switching of high-speed modulated LEDs, which affect signal quality. An optical smoothing compensation method is designed to effectively suppress these transient disturbances. Specifically, an optical buffer layer is added to the back end of the LED driver circuit. This buffer layer is composed of a fluorescent material or an optical resonant cavity with specific time response characteristics. When the LED output light intensity changes abruptly, the buffer layer can absorb excess energy or release stored energy, thereby smoothing the light output in the time domain. The compensation system needs to precisely match the LED modulation rate, and the optimal smoothing effect is achieved by adjusting the thickness, material composition, and optical structure parameters of the buffer layer.

[0019] This invention addresses the non-uniformity of high-speed modulated LEDs across different wavelengths and spatial distributions by developing a multilayer penetrating optical supplementation structure. This structure is composed of multiple optical thin films with different refractive indices and absorption characteristics stacked together. When the modulated light signal passes through these thin film layers, selective transmission, reflection, and scattering occur. By carefully designing the optical parameters of each layer, the supplementary light can penetrate to different depths within the LED emitting region, reconstructing and optimizing the internal light field distribution. The penetrating supplementation not only acts on the LED surface but also penetrates deep into the active layer, compensating for the uneven carrier distribution and local light intensity loss caused by high-speed modulation.

[0020] This invention takes into account the inherent response inertia of LEDs during high-speed modulation, which mainly stems from the limitations of carrier lifetime and device parasitic capacitance. This inertia causes the light output to fail to follow changes in the electrical signal in real time. To address this, an optical inertia compensation mechanism is designed. An optical system with negative delay characteristics is used to preprocess the LED output. This system includes a nonlinear optical crystal and a fast optical switching device, which can inject or suppress light energy in advance based on the derivative information of the modulation signal. Through this feedforward optical compensation method, the response inertia of the LED itself can be effectively offset, making the actual light output closer to the ideal modulation waveform.

[0021] This invention takes into account that during high-speed modulation, the light field distribution disturbance similar to eddy currents will be generated inside the LED chip. This vortex-shaped light field will cause distortion and mode instability of the far-field light spot. To this end, an optical compensation method based on orbital angular momentum is developed to suppress this eddy current effect. By placing a spiral phase plate or spatial light modulator on the light output path of the LED, a specific phase modulation is applied to the output light field to generate a compensation light field with the opposite phase to the eddy current inside the LED. The two are superimposed to cancel the eddy current component and restore the regular light field distribution. The compensation system needs to detect the phase distribution of the light field in real time and dynamically adjust the compensation parameters to adapt to different modulation frequencies.

[0022] To improve the energy utilization efficiency of high-speed modulated LEDs, this invention designs a circulating optical supplementation system. This system captures the scattered and edge light from the LED that is not effectively utilized through a specially designed optical collector. After processing by an optical delay line and a wavelength converter, the light is reinjected into the main optical path of the LED to form a supplementary light source. During the circulation process, the optical delay is precisely controlled to match the circulating light with the current modulation state, avoiding interference noise. The entire circulation system forms a closed-loop optical feedback, which can adaptively adjust the intensity and phase of the supplementary light according to the real-time state of the modulation signal, achieving a dynamically optimized optical supplementation effect.

[0023] This invention establishes a multi-dimensional identification system for high-speed modulated LED output signals. Through various means such as spectral analysis, time-domain waveform detection, and spatial light field measurement, it identifies the type and degree of distortion in the modulated signal in real time. The identification system can distinguish between distortion caused by the driving circuit, nonlinearity caused by the characteristics of the LED device itself, and interference caused by external environmental factors. Based on the identification results, the adaptive optics compensation module selects the corresponding compensation strategy and adopts different optical compensation schemes for different types of distortion, including intensity compensation, phase correction, and spectral shaping, to ensure the pertinence and effectiveness of the compensation effect.

[0024] This invention introduces a fault-tolerant mechanism into a high-speed modulated LED system, designing a redundant optical supplement architecture to cope with device failures or performance degradation. This architecture includes multiple parallel optical supplement channels, each employing a different supplement principle and implementation method. Under normal operation, the channels work together to provide the optimal supplement effect. When a fault or poor supplement effect is detected in a certain channel, the system can automatically switch to a backup channel or reallocate the supplement weights of each channel. The fault-tolerant system also includes a real-time performance evaluation module that continuously monitors the working status and output quality of each supplement channel, providing a basis for fault-tolerant decisions.

[0025] This invention addresses the diverse needs of high-speed modulated LEDs in different application scenarios by developing an integrated multi-mode optical supplementation method. This method integrates multiple supplementation modes such as intensity supplementation, spectral supplementation, spatial supplementation, and temporal supplementation. Based on actual modulation parameters and performance requirements, the system can flexibly select a single mode or a combination of multiple modes for supplementation. The integrated system includes functional modules such as a mode selector, parameter optimizer, and effect evaluator. By analyzing historical data and current operating status through machine learning algorithms, it automatically determines the optimal combination of supplementation modes and the weight allocation of each mode, achieving a multi-dimensional synergistic optical supplementation effect.

[0026] To address the output interruption problem of high-speed modulated LEDs during sudden failures or performance fluctuations, this invention develops a backup and supplement technology based on optical state storage. This technology utilizes an optical storage medium to record key information such as the light field distribution, spectral characteristics, and time-domain waveform of the LED under normal operating conditions. When an abnormal LED output is detected, the system can quickly read the stored optical state information and reconstruct a supplementary light field similar to the normal state through programmable optical devices, temporarily replacing or supplementing the abnormal LED output. The backup system also has a learning function, which can continuously update the stored optical state library to adapt to the aging of LED devices and changes in operating conditions. Attached Figure Description

[0027] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0028] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0029] It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases the steps shown or described may be executed in a different order than that shown here.

[0030] like Figure 1 As shown, this embodiment provides an optical compensation method suitable for high-speed modulated LEDs, including: The real-time modulation signal amplitude of the high-speed modulation LED is obtained, and the nonlinear attenuation characteristics exceeding the threshold are extracted. A preset threshold detection algorithm is used to determine the amplitude change trend and obtain the attenuation compensation requirement. Based on the attenuation compensation requirement, the initial parameters of the optical supplement injection are determined. Based on the light intensity fluctuation suppression requirement, the transient disturbance energy is absorbed through the optical smoothing buffer layer to obtain the smoothed light output waveform. The uneven light field distribution features are extracted from the smoothed light output waveform, and the internal light field is reconstructed using a multi-layer permeation structure to obtain optimized light field distribution data. Based on the optimized optical field distribution data, the necessity of response inertial compensation is determined. If carrier lifetime delay is detected, compensation energy is injected through the negative delay system feedforward to obtain an output signal that closely approximates the ideal waveform. The phase information of eddy current disturbance is obtained from the output signal, and the opposite phase is applied to cancel the eddy component by using a phase modulation algorithm to obtain the optical field distribution result. Based on the light field distribution results, an optical path loop feedback path is constructed to capture scattered light. After processing by a delay line, the light is injected to supplement the loop light source data. Multi-dimensional identification is performed based on cyclic light source data, and the distortion type is identified by the support vector machine algorithm to obtain the parameters of the adaptive compensation selection scheme. Based on the parameters of the adaptive compensation scheme, the channel status of the redundant architecture is determined. If the main channel fails, the weight is assigned to the backup channel to obtain the supplementary output after fault tolerance. The multi-mode set is fused from the fault-tolerant supplementary output, the weights are adjusted by the parameter optimizer to obtain the optimized multi-mode supplementary signal, and the abnormal part is reconstructed by the state storage backup to obtain the final stable LED output.

[0031] Furthermore, the process of obtaining the attenuation compensation requirement includes: The modulation signal of the high-speed modulation light-emitting diode is acquired, the signal amplitude data is recorded by a real-time acquisition device, and the acquired signal is initially filtered by digital signal processing technology to obtain smoothed signal amplitude data. Based on the smoothed signal amplitude data, a preset threshold is applied for preliminary screening. If the signal amplitude exceeds the preset threshold, it is marked as an anomaly, and the location and amplitude value of the anomaly are recorded to obtain the distribution results of the abnormal signal. Based on the distribution of abnormal signals, feature data of nonlinear attenuation are extracted. By performing time-series analysis on the amplitude changes of abnormal points, the trend direction and rate of amplitude changes are determined, and the quantitative results of the change trend are obtained. Based on the quantitative results of the changing trend, analyze the correspondence between the characteristics of nonlinear decay and the amplitude change. If the changing trend shows a continuous decline, record the duration of the decline and the range of amplitude reduction to determine the severity of the decay characteristics. Based on the severity of the attenuation characteristics, the parameters of the compensation requirement are calculated, and the attenuation characteristics are matched with a preset compensation model to obtain the adjustment value of the compensation requirement. Based on the adjustment value of the compensation requirement, the driving parameters of the modulation signal are dynamically corrected, and a real-time feedback mechanism is used to monitor the amplitude of the corrected signal to determine whether the compensated signal has recovered to the expected range. Based on the monitoring results, if the compensated signal amplitude still does not reach the expected range, the parameters of the compensation model are fine-tuned, and the signal amplitude is further optimized through iterative processing to obtain the final modulated signal output.

[0032] Furthermore, in this embodiment, real-time acquisition of signal amplitude is a fundamental step in the signal processing of high-speed modulated LEDs. Assuming a high-precision acquisition device is used to record signal amplitude data at a sampling rate of 1000 times per second, the initial data may contain noise interference. Preliminary filtering is performed using digital signal processing techniques, such as a moving average filter, averaging every 10 sampling points to obtain smoothed signal amplitude data. This method effectively reduces the impact of random noise, making the signal curve smoother and laying the foundation for subsequent analysis.

[0033] Specifically, based on the smoothed signal data, a preset threshold is set to filter out anomalies. Assuming the normal signal amplitude range is between 0.5 and 1.5 volts, and the preset threshold is 1.8 volts, if the amplitude reaches 2.0 volts, it is marked as an anomaly, and its time position (e.g., 500 milliseconds) and amplitude value (2.0 volts) are recorded. By statistically analyzing the distribution of anomalies, it can be found that they are concentrated in the initial stage of the signal, possibly due to unstable initial drive current.

[0034] In one embodiment, when extracting nonlinear attenuation features from anomaly signal distributions, it is assumed that the amplitude at the anomaly point gradually decreases from 2.0 volts to 1.6 volts over a duration of 200 milliseconds. Time-series analysis reveals a continuous decreasing trend in amplitude, with a decrease rate of approximately 0.002 volts per millisecond. This quantification result indicates that the signal exhibits significant nonlinear attenuation characteristics, requiring further analysis to determine the severity of the attenuation.

[0035] For example, regarding the correspondence between attenuation characteristics and amplitude changes, if the drop duration is 200 milliseconds and the amplitude decrease is 0.4 volts, the attenuation characteristic can be determined to be severe. Based on this, compensation requirement parameters are calculated. Assuming that a preset compensation model is used for matching, the adjustment value is to increase the drive current by 0.1 amperes to improve the signal amplitude. By dynamically correcting the drive parameters and monitoring the signal amplitude after compensation in real time, if it is found that the expected range, such as 1.5 volts, is still not reached, the compensation model parameters are fine-tuned, for example, by further increasing the current by 0.05 amperes. After iterative optimization, the final signal amplitude stabilizes at 1.4 volts, close to the expectation.

[0036] Specifically, the real-time feedback mechanism involved in this embodiment plays a crucial role in monitoring. Assuming the signal amplitude fluctuation range is reduced to within 0.1 volts after compensation, it indicates a significant correction effect. This method effectively improves signal stability, extends the lifespan of LEDs, and ensures the transmission quality of the modulated signal. Through the above multi-stage collaborative processing, from filtering to compensation to optimization, a complete closed loop is formed, ensuring that the signal output meets expectations and demonstrating high technical applicability.

[0037] Furthermore, the process of obtaining the smoothed light output waveform includes: By acquiring data for attenuation compensation, the real-time attenuation value of the optical system is obtained, and the compensation reference value of the current system is determined. Based on the compensation reference value, the initial parameter configuration for optical injection is calculated to obtain a set of parameters suitable for the current light intensity fluctuation; Based on the parameter set, a preset optical model is used for simulation to determine whether the light intensity fluctuation is within an acceptable range. If it exceeds the preset threshold, the key values ​​in the parameter set are adjusted to obtain the optimized parameter configuration. Based on the optimized parameter configuration, control data suitable for smooth buffering is extracted to determine the energy absorption strategy for transient disturbances. Based on the energy absorption strategy, the working mode of the optical smoothing buffer layer is deployed to obtain real-time energy data of transient disturbances, determine whether the preset absorption effect has been achieved, and if not, dynamically adjust the buffer layer to obtain a stable energy control result. Based on the energy control results, smoothed light output waveform data is generated to determine the smoothness of the final light output wave. Based on the smoothness of the light output wave, relevant data is recorded to the system log to obtain complete operating status information and determine whether further optimization is needed.

[0038] Furthermore, in this embodiment, during the attenuation compensation data acquisition process of the optical system, a high-precision optical power meter is used to monitor the intensity changes of the optical signal in real time. Suppose that in a certain scenario, the optical power meter detects that the attenuation value of the optical signal drops from an initial 100 milliwatts to 80 milliwatts, indicating a 20% attenuation in the system. At this point, when determining the compensation benchmark value, 80 milliwatts can be used as a reference point. Combined with historical data analysis, it can be inferred that the system may be affected by factors such as ambient temperature or device aging. In this way, the determination of the benchmark value not only reflects the current state but also provides a reliable basis for subsequent parameter configuration.

[0039] For example, in this embodiment, when calculating the initial parameter configuration for optical injection, the initial power of the injected light source can be set to 120 milliwatts based on the attenuation value and a reference value to compensate for a 20% loss. Furthermore, by analyzing historical data on light intensity fluctuations, assuming the fluctuation range is typically between ±5 milliwatts, the parameter set can include multiple configuration schemes with a power adjustment step size of 2 milliwatts. This method can flexibly handle fluctuations of varying degrees, ensuring the stability of system operation.

[0040] For example, in this embodiment, when simulating the parameter set using a preset optical model, it can simulate whether the light intensity fluctuation is within an acceptable range of ±3 milliwatts. If the simulation results show that the fluctuation reaches ±6 milliwatts, the parameter set needs to be adjusted, the power step size reduced to 1 milliwatt, and the simulation repeated until the fluctuation is controlled within the target range. This simulation adjustment method helps to identify potential problems in advance.

[0041] For example, in this embodiment, when extracting control data from the smoothing buffer, a control strategy with a power adjustment frequency of 10 times per second can be selected from the optimized parameter configuration to cope with transient disturbances. Assuming the peak energy of the transient disturbance is 50 millijoules, the energy absorption strategy can prioritize the high-frequency response capability of the buffer layer to ensure that the peak energy is effectively reduced. This strategy can improve the system's response speed.

[0042] For example, in this embodiment, when deploying the optical smoothing buffer layer, the working state of the buffer layer can be set to a high absorption mode based on real-time energy data. Suppose the detected transient disturbance energy data is 40 millijoules, which does not achieve the preset absorption effect of 30 millijoules. The buffer layer level is then dynamically adjusted, increasing the absorption layer thickness or changing the material properties until energy control stabilizes. This dynamic adjustment can effectively cope with sudden changes.

[0043] For example, in this embodiment, when generating smoothed light output waveform data, the smoothness of the waveform curve can be analyzed. Assuming the waveform jitter amplitude decreases from the initial 10% to 2%, it indicates a significant smoothing effect. This data recording method provides an intuitive basis for subsequent optimization.

[0044] For example, in this embodiment, when recording operational status information to the system log, key data such as the smoothness of the optical output waveform and the trend of attenuation value changes can be stored in a time-series format. Assuming the log shows that the number of system fluctuations decreased by 50% in the past hour, it indicates that the current configuration has good adaptability. This recording method facilitates subsequent analysis and optimization decisions.

[0045] Furthermore, the process of obtaining optimized light field distribution data includes: By performing preliminary analysis on the data of the light output wave, key point information in the waveform is obtained, and initial light field distribution data is obtained. Based on the initial light field distribution data, a smoothing process is used to denoise the waveform and determine the smoothed light output waveform data. Based on the smoothed light output waveform data, the non-uniformity in the light field distribution is extracted to determine whether there are significant non-uniform regions. If the non-uniformity exceeds the preset threshold, the corresponding distribution characteristic data is recorded. By performing layered analysis of the internal light field data through a multi-layered permeation structure, the distribution characteristics of each layer are obtained, resulting in layered light field distribution data. The layered light field distribution data is reconstructed to adjust the distribution characteristics of the internal light field and generate preliminary optimized light field data. The structural analysis method was used to verify the preliminary optimized light field data, obtain the optimized distribution characteristics, and determine the final optimized light field data. The final optimized light field data is stored and formatted to generate optimized light field distribution data that can be used for subsequent analysis.

[0046] Furthermore, in this embodiment, during the initial analysis of the optical output wave data, key point information is obtained by sampling the waveform signal. Assuming that in a single optical field test, the sampling frequency is set to 1000 times per second, the peak and valley points in the acquired waveform data are marked as key points. These points reflect the initial state of the optical field distribution. This analytical method helps to quickly locate the main feature regions in the optical field, laying the foundation for subsequent processing.

[0047] For example, in this embodiment, when using smoothing techniques to denoise the waveform, the waveform data is segmented using a time window averaging method. Assuming the window size is 5 sampling points, short-term noise interference is eliminated by averaging the data within each window. This method can effectively reduce random fluctuations in the waveform, resulting in more stable optical output waveform data and providing a reliable basis for subsequent analysis.

[0048] For example, in this embodiment, when extracting non-uniformity characteristics from smoothed light output waveform data, the uniformity of the light field distribution is detected by dividing the light field into regions. Assuming the light field is divided into 10 sub-regions, the average light intensity of each region is calculated. If the light intensity deviation of a certain region exceeds a preset threshold of 10%, that region is recorded as a non-uniform region. This detection method helps to accurately locate problem areas and improves the targeting of subsequent optimizations.

[0049] For example, in this embodiment, when performing layered analysis using a multi-layered permeable structure, the light field data is divided into three layers according to depth, and the distribution characteristics of each layer are analyzed separately. Assuming that the light intensity distribution in the first layer is concentrated in the central region, while the distribution in the third layer is more dispersed, this layered approach allows for a clear understanding of the changes in the light field at different depths, providing data support for subsequent reconstruction.

[0050] For example, in this embodiment, when performing light field distribution reconstruction processing, the light field distribution characteristics are adjusted based on the layered data. For instance, for the dispersed third layer, the light intensity weight in the central region is increased to make the overall distribution more uniform. This reconstruction method improves the overall consistency of the light field, laying the foundation for final optimization.

[0051] For example, in this embodiment, when performing structural analysis to verify the initially optimized light field data, the effect is confirmed by comparing the distribution characteristics before and after optimization. Assuming the proportion of non-uniform regions decreases from 20% to 5% after optimization, it indicates a significant optimization effect. This verification method helps ensure the reliability of the light field data.

[0052] For example, in this embodiment, when storing and formatting the final light field optimization data, the data is categorized and stored according to timestamps and converted into a common format. Assuming a storage cycle of once per hour, this ensures data traceability and ease of subsequent retrieval. This processing method improves data management efficiency and facilitates long-term analysis.

[0053] Furthermore, the process of obtaining an output signal that closely approximates the ideal waveform includes: By analyzing the light field distribution data, preliminary characteristics of the response inertia can be obtained to determine whether there is an abnormal delay phenomenon. If a carrier lifetime delay is detected in the response inertia, the delay detection module is triggered to obtain the specific delay parameters and determine whether the delay exceeds the preset threshold range. Based on the delay detection results, the negative delay system is activated, the required feedforward injection parameters are calculated, and the initial configuration scheme for compensation energy is obtained. Based on the initial configuration scheme of the compensation energy, the execution strategy of feedforward injection is adjusted, the adjusted energy injection data is obtained, and the final energy compensation value is determined. The final energy compensation value is input into the signal processing module to generate signal data that approximates the ideal waveform, resulting in an optimized output signal. Key features are extracted from the optimized output signal, and combined with the system's optimized feedback mechanism, subsequent parameters of signal processing are adjusted to determine the final output signal.

[0054] Furthermore, in this embodiment, when analyzing optical field distribution data to obtain preliminary characteristics of response inertia, the time-domain response of the optical field signal is monitored to identify the time delay from input to output. For example, if a signal response time of 50 microseconds is detected in a certain optical field distribution data, while the expected standard is 30 microseconds, this deviation may indicate a potential abnormal delay. To address this phenomenon, this embodiment further analyzes the dynamic characteristics of carrier motion in the optical field to determine if there is a carrier lifetime delay issue. This analysis is typically based on the spatiotemporal characteristics of the optical field distribution, combined with historical data comparison, to clarify the source of the delay.

[0055] For example, if a carrier lifetime delay is detected, the delay detection module is triggered to obtain the specific delay parameters. Suppose that the module analysis reveals a delay parameter of 80 microseconds, exceeding the preset threshold of 60 microseconds. In this case, the specific delay distribution area and its impact range need to be recorded. The delay detection module can subdivide the specific location of the delay based on the local characteristics of the light field distribution; for example, the delay may be more significant at the edge of the light field, while it may be relatively stable in the central region. This refined analysis helps in subsequent targeted adjustments.

[0056] For example, in this embodiment, when activating the negative delay system, a compensation energy scheme is designed by calculating the feedforward injection parameters. Assuming system analysis indicates that an additional 10 units of energy are needed to offset the delay effect, the initial configuration scheme will prioritize energy allocation to the edge regions with severe delays based on the intensity differences in the optical field distribution. This feedforward injection strategy aims to ensure the synchronization of the overall optical field response by compensating for signal delay in advance.

[0057] For example, in this embodiment, when adjusting the feedforward injection strategy for the initial configuration of the compensation energy, the energy injection value is fine-tuned from 10 units to 12 units based on real-time optical field feedback data to cover possible fluctuations. The adjusted energy injection data will be combined with the dynamic changes in the optical field distribution to ensure a more balanced compensation effect. The final determined energy compensation value will serve as a core parameter for system operation.

[0058] For example, in this embodiment, when the final energy compensation value is input into the signal processing module, signal data with a waveform close to the ideal is generated. Assume that after compensation, the rise time of the signal waveform is shortened from 50 microseconds to 32 microseconds, close to the ideal value of 30 microseconds. This optimized output signal can better match system requirements and improve the overall consistency of the light field distribution.

[0059] For example, in this embodiment, when extracting key features from the optimized output signal and adjusting parameters using a feedback mechanism, attention is paid to the peak intensity and distribution uniformity of the signal. Assuming the peak intensity deviation decreases from 5% to 2%, subsequent parameter adjustments will further optimize the signal processing algorithm, ensuring that the final signal output quality meets the expected standards. This feedback mechanism can continuously improve the response characteristics of the light field distribution.

[0060] For example, throughout the entire process, the analysis and adjustment of the optical field distribution data in this embodiment revolves around improving signal response speed and distribution uniformity. By refining the processing of multiple technical aspects such as response inertia, delay parameters, and energy compensation, the operational stability of the optical field system can be effectively improved. This multi-layered optimization strategy not only addresses complex delay issues but also provides reliable data support for subsequent optical field applications.

[0061] Furthermore, the process of obtaining the light field distribution results includes: Phase data of eddy current disturbance is obtained from the output signal, and key phase information is separated using signal processing techniques to obtain a preliminary phase distribution. Based on the preliminary phase distribution, phase modulation technology is used to generate the corresponding reverse phase signal and determine the parameter values ​​of the reverse phase. If the phase deviation of the generated reverse phase signal exceeds the preset threshold, the reverse phase is recalculated by adjusting the modulation parameters to obtain the reverse phase signal. The core data for canceling vortex components is extracted from the reverse phase signal and combined with the original output signal through superposition processing to obtain preliminary optical field adjustment results; Based on the preliminary light field adjustment results, the stability index of the light field distribution is obtained. If the stability index does not meet the preset standard, the phase superposition is further optimized through iterative processing to determine the final stability data. Based on the final stability data, a stable light field distribution is generated, the vortex component is canceled out, and the target light field distribution is obtained. Based on the target light field distribution, the distribution parameters are recorded and saved to generate a light field control scheme that can be used later.

[0062] Furthermore, in this embodiment, when analyzing the eddy current disturbance phase data in the output signal, the phase fluctuation of the original signal can be obtained through a signal acquisition device. Suppose that in a certain optical field system, a phase fluctuation range of ±5 degrees is detected, exceeding the expected stable value threshold of ±2 degrees. In this case, it is necessary to separate the key phase information. Frequency domain analysis techniques can be used to decompose the signal into different frequency components, extracting low-frequency data related to the eddy current disturbance to form a preliminary phase distribution. This approach helps to focus on the main source of the disturbance, providing a clear basis for subsequent processing.

[0063] For example, in this embodiment, when generating a reverse phase signal using phase modulation technology, a reverse waveform parameter is designed based on the initial phase distribution. Assuming the detected disturbance phase is positive 3 degrees, the modulation system will generate a reverse signal with negative 3 degrees to achieve phase cancellation.

[0064] It should be noted that if the initially generated reverse signal has a large deviation, such as a deviation of 0.5 degrees after actual measurement, the gain parameters of the modulation system can be adjusted to recalculate a more accurate reverse signal until the deviation is controlled within 0.1 degrees. This fine-tuning can significantly improve the accuracy of phase matching.

[0065] For example, in this embodiment, when extracting and superimposing the core data for offsetting vortex components, the precise reverse signal and the original signal are synthesized in the time domain. Assuming the vortex component accounts for 10% in the original signal, it can be reduced to below 2% after superposition. This processing method can effectively smooth irregular fluctuations in the optical field, laying the foundation for subsequent stability assessment.

[0066] For example, in evaluating the stability index of the light field distribution in this embodiment, if the preset standard is a volatility of less than 1%, while the actual measured value is 1.5%, then iterative processing is needed to optimize the phase superposition parameters. This embodiment can gradually adjust the amplitude and phase shift of the reverse signal, for example, by 0.2 degrees each time, until the volatility drops to 0.8%. This iterative method can gradually approach the ideal state, ensuring the stability of the light field distribution.

[0067] For example, in this embodiment, when generating a stable light field distribution and completing vortex component cancellation, the final light field parameters can be recorded in real time using a monitoring system. Assuming the final volatility stabilizes at 0.7%, the target light field distribution can be considered achieved. This stable distribution provides a reliable foundation for subsequent applications.

[0068] For example, this embodiment generates a scheme for saving and controlling the target light field distribution parameters. Key data can be stored in the system database to form a callable template. For instance, if the light field parameters in a certain experiment have a phase deviation of 0.1 degrees and a fluctuation rate of 0.7%, this can be saved as a standard scheme for direct use in similar future scenarios. This recording method significantly improves system reuse efficiency and reduces repeated debugging time.

[0069] By implementing these methods, a complete chain can be formed from phase data acquisition to the final optical field control scheme, ensuring that each step is processed specifically, and improving the overall effect through parameter adjustment and iterative optimization. This approach has significant application value in the field of optical field manipulation.

[0070] Furthermore, the process of obtaining the circulating light source data includes: The light field distribution data is collected and stored to construct an initial light field distribution dataset. The data is then classified and organized using a pre-established analysis model to obtain structured light field distribution information. Based on the structured light field distribution information, a cyclic feedback mechanism is designed to simulate and calculate the optical path, thereby obtaining the geometric layout and parameter configuration of the feedback path. Based on the geometric layout of the feedback path, a scattering capture operation is performed to obtain scattered light signal data. The captured data is then filtered to obtain a clear scattered light signal. The scattered light signal is subjected to a delay processing procedure. The delay line technology is used to adjust the time of the signal. The time offset of the signal is judged. If the offset exceeds the preset threshold, the correction process is performed to obtain the adjusted signal data. An injection and replenishment operation is performed on the adjusted signal data, and the processed signal is re-injected into the circulating light source system to obtain the replenished light source signal intensity information. Based on the supplemented light source signal intensity information, the light source data is optimized and classified using the support vector machine algorithm to determine the final configuration of the cyclic light source data. Based on the final configuration of the circulating light source data, complete operating parameters for the circulating light source are generated, and the operating parameters are stored and updated to obtain circulating light source data for subsequent light field optimization.

[0071] Furthermore, in this embodiment, during the acquisition and storage of light field distribution data, high-precision sensors are used to monitor the light field signal in real time. Assuming the acquired light field data covers the visible spectrum with a wavelength range of 400-700 nm, the acquisition frequency is set to 100 times per second to ensure data comprehensiveness. The acquired data is stored in a cloud database, forming an initial light field distribution dataset. This approach provides a rich data foundation for subsequent analysis.

[0072] For example, in this embodiment, when constructing the analysis model to classify and organize the data, a machine learning-based clustering method is used to group the light field distribution data according to features such as intensity and phase. For instance, the data is divided into two categories: high-intensity regions and low-intensity regions, each corresponding to different optical path characteristics. This classification helps to quickly identify key regions in the light field, providing direction for subsequent optimization.

[0073] For example, in this embodiment, the design of the cyclic feedback mechanism uses simulation software to virtually calculate the optical path and determine the geometric layout of the feedback path. Assuming the optical path length is 2.5 meters and the feedback angle is adjusted to 30 degrees to reduce signal loss, this layout design effectively improves the circulation efficiency of the optical signal.

[0074] For example, in this embodiment, a highly sensitive photodetector is used to capture the scattered light signal during the scattering capture operation. Assuming the captured signal-to-noise ratio is 3:1, after filtering using signal processing tools, the noise ratio is reduced to 1:5, resulting in clearer signal data. This processing method helps improve signal availability.

[0075] For example, in this embodiment, when adjusting the signal time using delay line technology during the delay processing flow, the delay time can be set to 10 nanoseconds. If a time offset of 15 nanoseconds is detected, exceeding the preset threshold of 5 nanoseconds, adjustment is performed through a correction circuit. This adjustment ensures signal synchronization, laying the foundation for subsequent injection operations.

[0076] For example, in this embodiment, during the signal injection supplementation operation, after the processed signal is re-injected into the circulating light source system, an increase of approximately 20% in the light source signal intensity can be detected. This supplementation operation can effectively enhance the stability of the light source and provide support for light field optimization.

[0077] For example, in this embodiment, when using the support vector machine algorithm to optimize and classify light source data, the data can be divided into high-efficiency and low-efficiency regions. Assuming the proportion of data in the high-efficiency region increases to 75%, the final configuration of the circulating light source data can be determined. This optimized classification can significantly improve the operating efficiency of the light source.

[0078] For example, in this embodiment, regarding the storage and updating of the final circulating light source operating parameters, the parameters can be saved to a local server and set to update automatically every 24 hours. This method ensures that the data is always up-to-date, providing a reliable basis for subsequent light field optimization.

[0079] Furthermore, the process of obtaining the parameters of the adaptive compensation selection scheme includes: Raw data is collected from a circulating light source and preprocessed to obtain a pre-cleaned dataset. Multi-dimensional feature information is extracted from the cleaned dataset, and feature selection methods are used to screen feature groups that are highly correlated with the distortion type to obtain key feature combinations. Based on key feature combinations, the support vector machine algorithm is applied to classify the distortion type. If the confidence of the classification result is lower than the preset threshold, the feature group is extracted a second time to obtain a more accurate classification result. Based on the classification results, analyze the distribution patterns of distortion types, obtain the influencing factors corresponding to the distortion types, and identify the main sources of distortion. Based on the main sources of distortion, the initial parameters for adaptive compensation are calculated, and a preliminary compensation scheme is obtained by combining the light source analysis data. The preliminary compensation plan is compared with the efficiency improvement target. If the matching degree of the compensation plan does not meet the preset standard, the parameters are fine-tuned to determine the final compensation parameter plan. The final compensation parameter scheme is used to process the cyclic light source data to obtain optimized light source output data.

[0080] Furthermore, in this embodiment, during the acquisition and processing of cyclic light source data, a high-precision sensor collects 1000 sets of light source signal data per second. These data contain multi-dimensional information such as intensity and phase. The initial data may contain noise interference, therefore preprocessing is required. This embodiment uses a time series smoothing method to remove outliers, obtaining a cleaned dataset. This approach lays a reliable foundation for subsequent analysis.

[0081] For example, regarding the extraction and filtering of multi-dimensional feature information, this embodiment extracts features such as light intensity volatility and signal periodicity from the cleaned data. Assuming that the correlation between light intensity volatility and distortion type is as high as 0.8, while the correlations of other features are less than 0.3, volatility is preferentially selected as the key feature combination. This filtering method can focus on core influencing factors and improve the accuracy of subsequent classification.

[0082] For example, when applying the Support Vector Machine (SVM) algorithm to classify distortion types, suppose the classification result shows that the confidence level of a certain type of distortion is only 0.6, which is lower than the preset threshold of 0.85. In this case, a secondary extraction can be performed on the feature set, adding the signal frequency distribution as a supplementary feature. After reclassification, the confidence level increases to 0.9. This secondary extraction mechanism can effectively improve the reliability of classification and provide a more accurate basis for subsequent analysis.

[0083] For example, in analyzing the distribution pattern of distortion types in this embodiment, it is assumed that a certain type of distortion is mainly concentrated in a specific region of the optical feedback path, which may be related to the time offset of the scattered light signal. By tracing the influencing factors, it is determined that the main source of distortion is insufficient delay line adjustment. This analytical method helps to accurately locate the root cause of the problem.

[0084] For example, when calculating the initial parameters for adaptive compensation, this embodiment, based on the distortion source data, assumes that the delay line time offset is 2 milliseconds, exceeding the threshold by 1 millisecond, and therefore initially sets the compensation parameter to a reverse adjustment of 1.5 milliseconds. This parameter design can provide a starting point for subsequent schemes.

[0085] For example, when comparing the initial compensation plan with the target, if the matching degree is only 70% and does not reach the preset 80% standard, the parameters are fine-tuned to 1.8 milliseconds offset compensation, and the final matching degree is improved to 85%. This fine-tuning method ensures that the plan is more in line with actual needs.

[0086] For example, after processing the cyclic light source data using the final compensation parameter scheme, assuming the light source output stability improves from the original 75% to 90%, the optimization effect is significant. This processing method can effectively achieve the efficiency improvement goal and provide data support for light field optimization.

[0087] Furthermore, the process of obtaining the fault-tolerant supplementary output includes: By using a pre-established monitoring model, real-time operating data of each channel in the redundant architecture is obtained. By continuously collecting the status information of the main channel and the backup channel, the current working status of the channel is determined. If the main channel's operating data exceeds the preset threshold range, it is determined that the main channel is faulty, triggering the switching mechanism to transfer the running task to the backup channel, thus obtaining the initial channel switching results. Based on the performance parameters of the backup channel, the output of the backup channel is dynamically adjusted using a preset weight allocation rule to generate adjusted output data. Based on the adjusted output data, detect whether there is missing data or abnormal fluctuations. If an anomaly is detected, smooth the output data to obtain a stable output result. Based on the output after smoothing, and combined with the fault tolerance processing logic, the completeness of the supplementary output is checked to determine whether it meets the business requirements, and the final output after verification is generated. From the final output after verification, extract key status information, update the channel status records of the redundant architecture, and obtain closed-loop data for status analysis. Based on the closed-loop data from the status analysis, the response speed of the switching mechanism is continuously optimized, generating a more efficient fault detection and fault-tolerant processing flow.

[0088] Furthermore, the monitoring model involved in this embodiment determines whether the channels are operating normally based on historical operating data and preset rules. The primary channel is responsible for main data transmission, while the backup channel serves as redundancy. The monitoring model collects the latency and data integrity indicators of both channels every 5 seconds. If the latency of the primary channel exceeds a preset threshold of 200 milliseconds, the system determines that it may be faulty and triggers a switching mechanism. This real-time monitoring method can quickly detect problems and ensure uninterrupted data transmission.

[0089] For example, this embodiment addresses the switching mechanism after a main channel failure by implementing task transfer through preset switching logic.

[0090] In one possible implementation, once a primary channel failure is confirmed, the system immediately transfers the task load to the backup channel, while simultaneously recording the switchover time and the cause of the failure. Assuming the backup channel's initial load capacity is 80%, the system will adjust the task allocation ratio to 70% based on its performance parameters to avoid overload. This dynamic adjustment effectively balances the load and maintains system stability.

[0091] For example, in the output data smoothing process, if the data output from the backup channel experiences abnormal fluctuations, such as a sudden packet loss rate reaching 10%, the system will activate a smoothing algorithm to interpolate historical data and fill in the missing parts, ensuring output continuity. This approach is particularly suitable for scenarios with high continuity requirements in light source data transmission, helping to maintain smooth business processes.

[0092] For example, integrity verification is a crucial step in ensuring that the final output meets business requirements. Suppose that in a business scenario focused on improving light source efficiency, the final output data needs to contain complete brightness distribution information. The system will compare the output content item by item with preset standards. If any missing data is found, it will extract supplementary data from historical records. This verification mechanism can improve data reliability and reduce business risks.

[0093] For example, the formation of closed-loop data for state analysis relies on the extraction and recording of key state information. The system stores the time, reason, and effect of each switch as a log, such as recording that a switch took 2 seconds and had a success rate of 95%, providing a basis for subsequent optimization. This closed-loop mechanism helps to continuously improve the system's responsiveness.

[0094] For example, optimizing the response speed of the switching mechanism is a direction for continuous improvement.

[0095] In one possible implementation, this embodiment can adjust the fault detection threshold based on closed-loop data analysis, for example, reducing the latency threshold from 200 milliseconds to 180 milliseconds, to detect potential problems earlier. This optimization can significantly improve the system's fault tolerance efficiency and provide more stable support for light source data processing.

[0096] Furthermore, the process of obtaining the final stable LED output includes: By integrating fault-tolerant data with supplementary data, an initial multi-modal set of data is obtained; Key signal features are extracted from the multi-mode set, and preliminary signal optimization results are obtained by using a preset weight parameter allocation method. Based on the preliminary signal optimization results, if an abnormal part is detected in the signal, it is compared with the backup data in the state storage to determine the scope and type of the abnormal part. Based on the type and scope of the anomaly, retrieve the corresponding backup data from the state storage, perform a backup reconstruction operation, and obtain the repaired signal data; Based on the repaired signal data, the weight parameters are adjusted to balance the contributions of the multi-mode set, and the final optimized signal content is determined. The optimized signal content is processed through signal integration to generate a stable LED output signal; Based on a stable LED output signal, a preset verification mechanism is used to determine whether the signal meets the output standard, thus confirming the final LED output signal.

[0097] Furthermore, in this embodiment, during the integration of fault-tolerant data and supplementary data, preliminary classification of multi-mode data is used to ensure data integrity and consistency. Assuming a redundant LED signal control system, fault-tolerant data originates from the real-time backup of the main channel, while supplementary data comes from the auxiliary input of the backup channel. During integration, the sampling frequency and timestamps of the two types of data can be aligned first, for example, by setting the sampling frequency to 10 times per second, ensuring data consistency in the time dimension. This approach helps avoid data bias during subsequent feature extraction.

[0098] For example, in this embodiment, when extracting key signal features, parameters such as brightness and frequency of the LED signal are analyzed. Assuming the brightness value range is set between 0 and 255, if the brightness value in the main channel data suddenly jumps to an abnormal value such as 300, this abnormal feature can be extracted. When using a preset weight parameter allocation method, the weight of the main channel can be set to 0.7, and the weight of the backup channel can be set to 0.3 to balance their contributions and obtain preliminary optimization results. This weight allocation method prioritizes the reliability of the main channel data while also taking into account the supplementary role of the backup channel.

[0099] For example, this embodiment compares detected anomalies in the signal optimization results with backup data in the state storage. If a sustained fluctuation in the brightness signal is detected within a certain period, exceeding the normal value by ±10%, the anomaly type can be identified as "signal jitter." Retrieving backup data for the corresponding time period from the state storage—for example, if the brightness value recorded by the backup channel is stable at around 200—confirms that the anomaly range is within 5 seconds of that time period. This comparison method helps to accurately pinpoint the problem area.

[0100] For example, in this embodiment, when performing a backup reconstruction operation, stable data from the state storage can be directly used to overwrite the data in the abnormal range. Assuming the brightness value fluctuates between 180 and 300 within the abnormal range, and the backup data is 200, the reconstructed signal data will be uniformly adjusted to 200, ensuring signal continuity. This operation can quickly repair signal anomalies and guarantee the stability of LED output.

[0101] For example, in this embodiment, when adjusting the weighting parameters to balance the contributions of the multi-mode set, the weights can be dynamically adjusted based on the stability of the repaired signal. If the backup channel data contributes significantly during the repair, the weight can be increased from 0.3 to 0.5. This dynamic adjustment method can better adapt to the real-time status of different channels and improve the reliability of the final optimized signal.

[0102] For example, in this embodiment, when generating a stable LED output signal through signal integration processing, the repaired signal can be smoothed. Assuming the brightness values ​​at adjacent time points are 198, 202, and 200, the average value of 200 can be taken as the final output signal. This method can reduce the impact of minor signal fluctuations on the LED display effect.

[0103] For example, in this embodiment, when determining whether a signal meets the output standard through a verification mechanism, the brightness output standard can be set to between 195 and 205. If the final signal value is 200, it is determined to meet the standard. This verification mechanism can ensure the quality of LED signal output and meet business requirements.

[0104] The above are merely preferred embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An optical supplemental method suitable for high speed modulation of LEDs, characterized in that, include: The real-time modulation signal amplitude of the high-speed modulation LED is obtained, and the nonlinear attenuation characteristics exceeding the threshold are extracted. A preset threshold detection algorithm is used to determine the amplitude change trend and obtain the attenuation compensation requirement. Based on the attenuation compensation requirement, the initial parameters of the optical supplement injection are determined. Based on the light intensity fluctuation suppression requirement, the transient disturbance energy is absorbed through the optical smoothing buffer layer to obtain the smoothed light output waveform. The uneven light field distribution features are extracted from the smoothed light output waveform, and the internal light field is reconstructed using a multi-layer permeation structure to obtain optimized light field distribution data. Based on the optimized optical field distribution data, the necessity of response inertial compensation is determined. If carrier lifetime delay is detected, compensation energy is injected through the negative delay system feedforward to obtain an output signal that closely approximates the ideal waveform. Based on the output signal, the phase information of the eddy current disturbance is obtained, and the opposite phase is applied to cancel the eddy component using a phase modulation algorithm to obtain the optical field distribution result. Based on the light field distribution results, an optical path loop feedback path is constructed to capture scattered light, which is then processed by a delay line and injected to supplement the light, thus obtaining loop light source data. Based on the circulating light source data, multi-dimensional identification is performed, and the distortion type is identified using the support vector machine algorithm to obtain the parameters of the adaptive compensation selection scheme. Based on the selected adaptive compensation scheme parameters, the channel status of the redundant architecture is determined. If the main channel fails, the weight is allocated to the backup channel to obtain the supplementary output after fault tolerance. The multi-mode set is fused from the fault-tolerant supplementary output, the weights are adjusted by the parameter optimizer to obtain the optimized multi-mode supplementary signal, and the abnormal part is reconstructed by the state storage backup to obtain the final stable LED output.

2. The method according to claim 1, characterized in that, The process of obtaining the attenuation compensation requirement includes: The modulation signal of the high-speed modulation light-emitting diode is acquired, the signal amplitude data is recorded by a real-time acquisition device, and the acquired signal is initially filtered by digital signal processing technology to obtain smoothed signal amplitude data. Based on the smoothed signal amplitude data, a preset threshold is applied for preliminary screening. If the signal amplitude exceeds the preset threshold, it is marked as an anomaly, and the location and amplitude value of the anomaly are recorded to obtain the distribution result of the abnormal signal. Based on the distribution results of the abnormal signals, the characteristic data of nonlinear attenuation are extracted. By performing time-series analysis on the amplitude changes of the abnormal points, the trend direction and rate of amplitude changes are determined, and the quantitative results of the change trend are obtained. Based on the quantification results of the changing trend, analyze the correspondence between the characteristics of nonlinear decay and the amplitude change. If the changing trend shows a continuous decline, record the duration of the decline and the range of amplitude reduction to determine the severity of the decay characteristics. Based on the severity of the attenuation characteristics, the parameters of the compensation requirement are calculated, and the attenuation characteristics are matched using a preset compensation model to obtain the adjustment value of the compensation requirement. Based on the adjustment value of the compensation requirement, the driving parameters of the modulation signal are dynamically corrected, and a real-time feedback mechanism is used to monitor the amplitude of the corrected signal to determine whether the compensated signal has recovered to the expected range. Based on the monitoring results, if the compensated signal amplitude still does not reach the expected range, the parameters of the compensation model are fine-tuned, and the signal amplitude is further optimized through iterative processing to obtain the final modulated signal output.

3. The method according to claim 1, characterized in that, The process of obtaining the smoothed light output waveform includes: By acquiring data for attenuation compensation, the real-time attenuation value of the optical system is obtained, and the compensation reference value of the current system is determined. Based on the compensation reference value, the initial parameter configuration for optical injection is calculated to obtain a set of parameters suitable for the current light intensity fluctuation; Based on the parameter set, a preset optical model is used for simulation to determine whether the light intensity fluctuation is within an acceptable range. If it exceeds the preset threshold, the key values ​​in the parameter set are adjusted to obtain the optimized parameter configuration. Based on the optimized parameter configuration, control data suitable for smooth buffering is extracted to determine the energy absorption strategy for transient disturbances. According to the energy absorption strategy, the working mode of deploying the optical smoothing buffer layer is used to obtain real-time energy data of transient disturbances, determine whether the preset absorption effect has been achieved, and if not, dynamically adjust the buffer layer to obtain a stable energy control result. Based on the energy control results, smoothed light output waveform data is generated, and the smoothness of the final light output wave is determined. Based on the smoothness of the light output wave, relevant data is recorded to the system log to obtain complete operating status information and determine whether further optimization is needed.

4. The method according to claim 1, characterized in that, The process of obtaining optimized light field distribution data includes: By performing preliminary analysis on the data of the light output wave, key point information in the waveform is obtained, and initial light field distribution data is obtained. Based on the initial light field distribution data, a smoothing process is used to denoise the waveform, and the smoothed light output waveform data is determined. Based on the smoothed light output waveform data, the non-uniformity in the light field distribution is extracted to determine whether there is a significant non-uniform region. If the non-uniformity exceeds a preset threshold, the corresponding distribution characteristic data is recorded. By performing layered analysis of the internal light field data through a multi-layered permeation structure, the distribution characteristics of each layer are obtained, resulting in layered light field distribution data. The layered light field distribution data is reconstructed to adjust the distribution characteristics of the internal light field and generate preliminary optimized light field data. The preliminary optimized light field data were verified using structural analysis methods to obtain the optimized distribution characteristics and determine the final optimized light field data. The final optimized light field data is stored and formatted to generate optimized light field distribution data that can be used for subsequent analysis.

5. The method according to claim 1, characterized in that, The process of obtaining an output signal that closely approximates the ideal waveform includes: By analyzing the light field distribution data, preliminary characteristics of the response inertia can be obtained to determine whether there is an abnormal delay phenomenon. If a carrier lifetime delay is detected in the response inertia, the delay detection module is triggered to obtain the specific delay parameters and determine whether the delay exceeds the preset threshold range. Based on the delay detection results, the negative delay system is activated, the required feedforward injection parameters are calculated, and the initial configuration scheme for compensation energy is obtained. Based on the initial configuration scheme of the compensation energy, the execution strategy of feedforward injection is adjusted, the adjusted energy injection data is obtained, and the final energy compensation value is determined. The final energy compensation value is input into the signal processing module to generate signal data that approximates the ideal waveform, thereby obtaining an optimized output signal. Key features are extracted from the optimized output signal, and combined with the system optimization feedback mechanism, subsequent parameters of signal processing are adjusted to determine the final output signal.

6. The method according to claim 1, characterized in that, The process of obtaining the light field distribution results includes: Phase data of eddy current disturbance is obtained from the output signal, and key phase information is separated using signal processing techniques to obtain a preliminary phase distribution. Based on the preliminary phase distribution, a corresponding reverse phase signal is generated using phase modulation technology, and the parameter values ​​of the reverse phase are determined. If the phase deviation of the generated reverse phase signal exceeds a preset threshold, the reverse phase is recalculated by adjusting the modulation parameters to obtain the reverse phase signal. The core data for canceling vortex components is extracted from the reverse phase signal and combined with the original output signal through superposition processing to obtain preliminary optical field adjustment results; Based on the preliminary light field adjustment results, the stability index of the light field distribution is obtained. If the stability index does not meet the preset standard, the phase superposition is further optimized through iterative processing to determine the final stability data. Based on the final stability data, a stable light field distribution is generated, the vortex component is canceled out, and the target light field distribution is obtained. Based on the target light field distribution, the distribution parameters are recorded and saved to generate a light field control scheme that can be called upon later.

7. The method according to claim 1, characterized in that, The process of obtaining circulating light source data includes: The light field distribution data is collected and stored to construct an initial light field distribution dataset. The data is then classified and organized using a pre-established analysis model to obtain structured light field distribution information. Based on the structured light field distribution information, a cyclic feedback mechanism is designed to simulate and calculate the light path, thereby obtaining the geometric layout and parameter configuration of the feedback path. Based on the geometric layout of the feedback path, a scattering capture operation is performed to obtain scattered light signal data. The captured data is then filtered to obtain a clear scattered light signal. The scattered light signal is subjected to a delay processing procedure. The delay line technology is used to adjust the time of the signal. The time offset of the signal is determined. If the offset exceeds a preset threshold, correction processing is performed to obtain the adjusted signal data. An injection and replenishment operation is performed on the adjusted signal data to re-inject the processed signal into the circulating light source system and obtain the replenished light source signal intensity information. Based on the supplemented light source signal intensity information, the light source data is optimized and classified using the support vector machine algorithm to determine the final cyclic light source data configuration. Based on the final circulating light source data configuration, complete circulating light source operating parameters are generated, and the operating parameters are stored and updated to obtain circulating light source data for subsequent light field optimization.

8. The method according to claim 1, characterized in that, The process of obtaining the parameters of the adaptive compensation selection scheme includes: Raw data is collected from a circulating light source and preprocessed to obtain a pre-cleaned dataset. Multi-dimensional feature information is extracted from the cleaned dataset, and feature selection methods are used to screen feature groups that are highly correlated with the distortion type to obtain key feature combinations. Based on the key feature combination, the support vector machine algorithm is applied to classify the distortion type. If the confidence of the classification result is lower than the preset threshold, the feature group is extracted a second time to obtain a more accurate classification result. Based on the classification results, analyze the distribution pattern of distortion types, obtain the influencing factors corresponding to the distortion types, and identify the main sources of distortion. Based on the main sources of distortion, the initial parameters for adaptive compensation are calculated, and a preliminary compensation scheme is obtained by combining the light source analysis data. The preliminary compensation scheme is compared with the efficiency improvement target. If the matching degree of the compensation scheme does not meet the preset standard, the parameters are fine-tuned to determine the final compensation parameter scheme. The cyclic light source data is processed using the aforementioned final compensation parameter scheme to obtain optimized light source output data.

9. The method according to claim 1, characterized in that, The process of obtaining the supplementary output after fault tolerance includes: By using a pre-established monitoring model, real-time operating data of each channel in the redundant architecture is obtained. By continuously collecting the status information of the main channel and the backup channel, the current working status of the channel is determined. If the main channel's operating data exceeds the preset threshold range, it is determined that the main channel is faulty, triggering the switching mechanism to transfer the running task to the backup channel, thus obtaining the initial channel switching results. Based on the performance parameters of the backup channel, the output of the backup channel is dynamically adjusted using a preset weight allocation rule to generate adjusted output data. Based on the adjusted output data, detect whether there is data missing or abnormal fluctuation. If an abnormality is detected, smooth the output data to obtain a stable output result. Based on the output after smoothing, and combined with the fault tolerance processing logic, the completeness of the supplementary output is checked to determine whether it meets the business requirements, and the final output after verification is generated. From the final output after verification, key status information is extracted, the channel status records of the redundant architecture are updated, and closed-loop data for status analysis is obtained. Based on the closed-loop data from the state analysis, the response speed of the switching mechanism is continuously optimized, generating a more efficient fault detection and fault-tolerant processing flow.

10. The method according to claim 1, characterized in that, The process of obtaining a final stable LED output includes: By integrating fault-tolerant data with supplementary data, an initial multi-modal set of data is obtained; Key signal features are extracted from the multi-mode set, and preliminary signal optimization results are obtained by using a preset weight parameter allocation method. Based on the preliminary signal optimization results, if an abnormal part is detected in the signal, the range and type of the abnormal part are determined by comparing it with the backup data in the state storage. Based on the type and scope of the abnormal part, the corresponding backup data is retrieved from the state storage, a backup reconstruction operation is performed, and the repaired signal data is obtained. Based on the repaired signal data, the weight parameters are adjusted to balance the contributions of the multi-mode set, and the final optimized signal content is determined. The optimized signal content is subjected to signal integration processing to generate a stable LED output signal; Based on the stable LED output signal, a preset verification mechanism is used to determine whether the signal meets the output standard, thus confirming the final LED output signal.

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