Anti-interference brightness adjustment method and system based on photosensitive signal analysis

Abstract

This invention discloses an anti-interference brightness adjustment method and system based on photosensitive signal analysis, relating to the field of brightness adjustment technology. The method includes: S1, periodically acquiring raw illumination monitoring data and performing preprocessing on the acquired data; S2, using a bandpass filter to limit the frequency range to obtain initial denoising data, further removing power supply ripple and noise to obtain effective illumination monitoring data; S3, evaluating the current brightness fluctuation level, extracting real-time effective illumination monitoring data to quantify LED brightness adjustment requirements, and adjusting the LED brightness output; and after circuit adjustment, determining whether a fault detection process is triggered based on the adjustment error; S4, evaluating and quantifying the effectiveness of data denoising, determining the cause of adjustment error, and realizing an anti-interference brightness adjustment closed loop. This solves the problem that existing technologies cannot effectively remove power supply ripple and noise from illumination monitoring data, leading to unstable LED brightness adjustment.

Description

Technical Field

[0001] This invention relates to the field of brightness adjustment technology, specifically to an anti-interference brightness adjustment method and system based on photosensitive signal analysis. Background Technology

[0002] With the continuous development of intelligent control technology, lighting adjustment systems have been widely used in indoor and outdoor lighting control, industrial automation, and other intelligent equipment fields. Current lighting adjustment technology mainly relies on sensors to monitor ambient light intensity and adjusts the brightness of the light source through electronic circuits to adapt to different usage needs.

[0003] For example, invention publication CN115426736B discloses an LED brightness adjustment circuit. This circuit includes a brightness adjustment module and a parameter adjustment module. The brightness adjustment module includes a light-emitting diode (LED) to be controlled and a transistor connected in series with the LED. The brightness adjustment module is used to adjust the brightness of the LED according to the input parameters of the transistor. The parameter adjustment module is used to connect to the I / O port of the main control chip through at least one signal input terminal and adjust the input parameters according to the output state of the I / O port. This eliminates the need for a dedicated I / O port on the main control chip to implement special functions, i.e., it eliminates the need for a dedicated chip. While providing multiple brightness level controls, it effectively reduces implementation costs, thereby improving the user experience and enhancing product value.

[0004] For example, the invention with publication number CN116156704A provides an indoor brightness adjustment system, belonging to the technical field of indoor control systems. This indoor brightness adjustment system includes an information acquisition unit for acquiring the start time of indoor use; a timing unit, communicatively connected to a control unit; a first acquisition unit for acquiring outdoor light brightness; a second acquisition unit for acquiring indoor light brightness; a light blocking unit, communicatively connected to the control unit, for turning indoor light blocking devices on or off according to the control unit's instructions; a light adjustment unit, communicatively connected to the control unit, for adjusting light brightness according to the control unit's instructions; and a control unit, communicatively connected to the information acquisition unit, the first acquisition unit, and the second acquisition unit. This invention can solve the problem that when the indoor environment is too dark or too bright, users feel that the indoor brightness is unsuitable when changing environments, which may even seriously affect the eye health of indoor occupants.

[0005] However, existing lighting control systems still face some challenges. In some application scenarios, interference from ambient light, light source status, and other factors makes it difficult for traditional brightness adjustment methods to achieve precise adjustment and stable brightness output. Especially when faced with complex circuits and multiple sensor signals, the system's anti-interference capability is weak, resulting in large brightness fluctuations and affecting the user experience.

[0006] Therefore, in order to address the above problems, there is an urgent need for an anti-interference brightness adjustment method and system based on photosensitive signal analysis. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this invention provides an anti-interference brightness adjustment method and system based on photosensitive signal analysis, which solves the problem that existing technologies cannot effectively remove power ripple and noise from illumination monitoring data, leading to unstable LED brightness adjustment.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solution: an anti-interference brightness adjustment method based on photosensitive signal analysis, comprising: S1, periodically acquiring raw illumination monitoring data, and performing multimodal temporal alignment, outlier removal, data missing interpolation and normalization processing on the acquired data to obtain preprocessed raw illumination monitoring data; S2, based on the preprocessed raw illumination monitoring data, using a bandpass filter to limit the frequency range to obtain initial denoised data, further removing power supply ripple and power supply noise, and performing weighted averaging processing on the denoised data to obtain effective illumination monitoring data; S3: Based on the effective illumination monitoring data, assess the current brightness fluctuation level and determine whether to enter the circuit adjustment process; after entering the circuit adjustment process, extract the real-time effective illumination monitoring data to quantify the LED brightness adjustment requirements, and convert the quantization result into a control current signal to adjust the LED brightness output; after the circuit adjustment is completed, determine whether to trigger the fault detection process based on the adjustment error; S4: After the fault detection process is triggered, extract the original illumination monitoring data and the effective illumination monitoring data, evaluate and quantify the effectiveness of data denoising, determine the cause of the adjustment error, and realize the anti-interference brightness adjustment closed loop.

[0009] Furthermore, raw illumination monitoring data is periodically collected, and multimodal time-series alignment, outlier removal, data missing interpolation, and normalization are performed on the collected data. The specific steps to obtain preprocessed raw illumination monitoring data are as follows: A fixed-width time window is set as one sampling period, and raw illumination monitoring data is periodically collected. The raw illumination monitoring data includes power supply ripple voltage, phototransistor output current, ambient light intensity, and power supply voltage. For the collected raw illumination monitoring data, the timestamps between different acquisition channels are uniformly aligned using a multimodal time-series alignment algorithm, and outliers and irregular fluctuations in the raw illumination monitoring data are automatically identified and removed using a quartile spacing algorithm. The missing data intervals are compensated and reconstructed using a local weighted regression interpolation algorithm, and the raw illumination monitoring data is then normalized and standardized using a Z-Score normalization algorithm.

[0010] Furthermore, based on the preprocessed raw illumination monitoring data, a bandpass filter is used to limit the frequency range to obtain initial denoised data, further removing power supply ripple and noise. The denoised data is then weighted and averaged to obtain effective illumination monitoring data. The specific steps are as follows: Extract the preprocessed raw illumination monitoring data and arrange them according to the sampling timestamp order. Bandpass filter is applied sequentially to the illumination monitoring data of each sampling point, using a bandpass filter with a cutoff frequency from N Hz to M Hz to filter out frequencies below N Hz and above M Hz. The noise is detected to obtain initial denoised data. The initial denoised data is then input into an FIR low-pass filter with a cutoff frequency of N Hz to remove power supply ripple, resulting in secondary denoised data. The secondary denoised data is then input into an IIR high-pass filter with a cutoff frequency of M Hz to remove power supply noise, resulting in denoised data. The denoised data is then extracted. For each data point, a weighted average is taken from the K data points before and after it. The weights of the data points before and after each other decrease as the distance increases, with points that are farther away having smaller weights. The sum of all weights is 1, resulting in effective illumination monitoring data.

[0011] Furthermore, based on the effective illumination monitoring data, the specific steps for assessing the current brightness fluctuation level and determining whether to enter the circuit adjustment process are as follows: extract the effective illumination monitoring data, compare the power supply ripple voltage and the power supply ripple threshold in real time, and calculate the brightness fluctuation assessment value when the power supply ripple voltage is greater than the power supply ripple threshold; compare the brightness fluctuation assessment value and the brightness fluctuation threshold in real time, and maintain the current circuit unchanged when the brightness fluctuation assessment value is less than or equal to the brightness fluctuation threshold; otherwise, enter the circuit adjustment process.

[0012] Further, the specific steps for calculating the brightness fluctuation assessment value are as follows: Extract the effective ambient light intensity and effective phototransistor output current for the most recent Q sampling periods, and calculate the average effective ambient light intensity and the average effective phototransistor output current respectively; calculate the power function value with the power supply ripple voltage as the exponent and the power supply ripple threshold as the power supply ripple influence coefficient, and add one to obtain the power supply ripple influence factor; calculate the absolute value of the difference between the effective ambient light intensity and the average effective ambient light intensity, and divide it by the average effective ambient light intensity plus a minimum term to obtain the light intensity fluctuation factor; calculate the absolute value of the difference between the effective phototransistor output current and the average effective phototransistor output current, and divide it by the average effective phototransistor output current plus a minimum term to obtain the photocurrent fluctuation factor; multiply the power supply ripple influence factor, the light intensity fluctuation factor, and the photocurrent fluctuation factor in sequence to obtain the brightness fluctuation assessment value.

[0013] Further, after entering the circuit adjustment process, the specific steps for extracting real-time effective illumination monitoring data to quantify LED brightness adjustment requirements are as follows: Extract real-time effective ambient light intensity, effective phototransistor output current, maximum effective phototransistor output current, and effective power supply ripple voltage. Add one to the product of effective phototransistor output current and effective ambient light intensity, and take the natural logarithm to obtain the illumination intensity adjustment factor. Take the negative of the difference between the effective power supply ripple voltage and the power supply ripple threshold as the exponent, take the exponential function value of the natural constant e, and add one to obtain the power supply ripple correction factor. Calculate the ratio of effective phototransistor output current to the maximum effective phototransistor output current to obtain the current standardization coefficient. Multiply the illumination intensity adjustment factor, power supply ripple correction factor, and current standardization coefficient sequentially to obtain the LED brightness adjustment value.

[0014] Further, the quantization result is converted into a control current signal to adjust the LED brightness output. After the circuit adjustment is completed, the specific steps for determining whether to trigger the fault detection process based on the adjustment error are as follows: A digital-to-analog converter is used to convert the calculated LED brightness adjustment value into a corresponding control current signal, and this control current signal is input to the adjustment terminal of the constant current source circuit to adjust the feedback resistor and the gate voltage of the MOSFET. After the circuit adjustment is completed, the adjustment error between the real-time transistor illumination intensity and the LED brightness adjustment value is calculated. When the adjustment error is less than the error threshold, the adjustment is considered complete, and the current circuit remains unchanged. When the adjustment error is greater than or equal to the error threshold, the adjustment is considered to have failed, and a new control current signal is generated and input to the adjustment terminal of the constant current source circuit for secondary circuit adjustment. If W consecutive circuit adjustments fail to bring the LED brightness to the LED brightness adjustment value, the fault detection process is triggered.

[0015] Furthermore, after triggering the fault detection process, the original illumination monitoring data and effective illumination monitoring data are extracted. The effectiveness of data denoising is evaluated and quantified to determine the cause of adjustment error. The specific steps to achieve the anti-interference brightness adjustment closed loop are as follows: After triggering the fault detection process, the original illumination monitoring data and effective illumination monitoring data are extracted, and the denoising response evaluation value is calculated. The denoising response evaluation value and the response threshold are compared in real time. When the denoising response evaluation value is greater than the response threshold, an alarm signal is automatically generated, prompting the user to check the power supply voltage and circuit components. When the denoising response evaluation value is less than or equal to the response threshold, it is determined that the data denoising is incomplete. The cutoff frequency range of the bandpass filter is adjusted, and data denoising is performed again. The circuit is readjusted based on the latest effective illumination monitoring data to complete the brightness adjustment closed loop.

[0016] Further, the specific steps for calculating the denoising response evaluation value are as follows: Divide the difference between the original ambient light intensity and the effective ambient light intensity by the standard deviation of the original ambient light intensity, take the sine of the comparison value and square it, then add one to obtain the light fluctuation adjustment term; Add one to the ratio of the original power supply ripple voltage to the power supply ripple threshold and take the natural logarithm to obtain the power supply ripple correction term; Calculate the ratio of the effective phototransistor output current to the original maximum value of the phototransistor output current to obtain the photocurrent influence term; Take the cosine of the ratio of the original maximum light intensity to the ambient light intensity and add one to obtain the maximum light intensity correction term; Multiply the light fluctuation adjustment term, the power supply ripple correction term, and the photocurrent influence term in sequence, and then divide the resulting product by the maximum light intensity correction term to obtain the denoising response evaluation value.

[0017] The second aspect of this invention provides an anti-interference brightness adjustment system based on photosensitive signal analysis, comprising: a data acquisition and preprocessing module, a light signal denoising module, a brightness adjustment and control module, and a fault monitoring and response module, wherein: the data acquisition and preprocessing module is used to periodically acquire raw light monitoring data and perform multimodal timing alignment, outlier removal, data missing interpolation, and normalization processing on the acquired data to obtain preprocessed raw light monitoring data; the light signal denoising module is used to obtain initial denoised data based on the preprocessed raw light monitoring data by using a bandpass filter to limit the frequency range, further removing power supply ripple and power supply noise, and performing weighted averaging on the denoised data. The system obtains effective illumination monitoring data; the brightness adjustment and control module is used to evaluate the current brightness fluctuation based on the effective illumination monitoring data and determine whether to enter the circuit adjustment process; after entering the circuit adjustment process, it extracts real-time effective illumination monitoring data to quantify the LED brightness adjustment requirements, and converts the quantization result into a control current signal to adjust the LED brightness output; after the circuit adjustment is completed, it determines whether to trigger the fault detection process based on the adjustment error; the fault monitoring and response module is used to extract the original illumination monitoring data and effective illumination monitoring data after the fault detection process is triggered, evaluate and quantify the effectiveness of data denoising, determine the cause of the adjustment error, and realize an anti-interference brightness adjustment closed loop.

[0018] The present invention has the following beneficial effects:

[0019] (1) An anti-interference brightness adjustment method and system based on photosensitive signal analysis can dynamically and accurately adjust the LED brightness by evaluating brightness fluctuations based on effective light monitoring data, ensuring consistent and stable brightness output under different ambient light conditions, and improving the flexibility and adaptability of light adjustment.

[0020] (2) Anti-interference brightness adjustment method and system based on photosensitive signal analysis. By comprehensively considering factors such as power supply ripple voltage and light intensity fluctuation, the present invention can effectively assess and mitigate the impact of external interference such as power supply noise and light intensity fluctuation on the system, ensure the stable operation of the system in complex environments, and avoid the impact of interference on brightness adjustment.

[0021] (3) The anti-interference brightness adjustment method and system based on photosensitive signal analysis introduces multiple safety mechanisms in the circuit adjustment and data processing process. When the system has problems, it can quickly switch to the fault detection mode and perform necessary repair operations, which effectively ensures the safety and stability of the system operation.

[0022] (4) The anti-interference brightness adjustment method and system based on photosensitive signal analysis has an intelligent fault detection process. By monitoring the error in the circuit adjustment process in real time and combining data denoising and component status evaluation, it can identify system faults in a timely manner and generate alarms, thereby ensuring that the system can respond quickly and make corrections when faults occur, and ensuring the efficient and reliable operation of the system. Attached Figure Description

[0023] Figure 1 The flowchart shows an anti-interference brightness adjustment method based on photosensitive signal analysis.

[0024] Figure 2 This is a structural diagram of an anti-interference brightness adjustment system based on photosensitive signal analysis;

[0025] Figure 3 A histogram for circuit adjustment decisions based on LED brightness adjustment values;

[0026] Figure 4 This is a schematic diagram of the brightness adjustment circuit layout based on photosensitive signal analysis. Detailed Implementation

[0027] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] Please see Figures 1-4This invention provides a technical solution: an anti-interference brightness adjustment method based on photosensitive signal analysis, comprising: S1, periodically collecting raw illumination monitoring data, and performing multimodal timing alignment, outlier removal, data missing interpolation, and normalization processing on the collected data to obtain preprocessed raw illumination monitoring data; S2, based on the preprocessed raw illumination monitoring data, using a bandpass filter to limit the frequency range to obtain initial denoising data, further removing power supply ripple and power supply noise, and performing weighted averaging processing on the denoised data to obtain effective illumination monitoring data; S3, evaluating the current brightness fluctuation level based on the effective illumination monitoring data to determine whether to enter the circuit adjustment process; after entering the circuit adjustment process, extracting real-time effective illumination monitoring data to quantify the LED brightness adjustment requirement, and converting the quantization result into a control current signal to adjust the LED brightness output; and after the circuit adjustment is completed, determining whether to trigger the fault detection process based on the adjustment error; S4, after triggering the fault detection process, extracting the raw illumination monitoring data and effective illumination monitoring data, evaluating and quantifying the effectiveness of data denoising, determining the cause of the adjustment error, and realizing a closed loop for anti-interference brightness adjustment.

[0029] Specifically, the raw illumination monitoring data is periodically collected, and multimodal timing alignment, outlier removal, data imputation, and normalization are performed on the collected data. The specific steps to obtain the preprocessed raw illumination monitoring data are as follows: A fixed-width time window is set as one sampling period, and raw illumination monitoring data is periodically collected. The raw illumination monitoring data includes power supply ripple voltage, power supply voltage, phototransistor output current, and ambient light intensity. Each data point is collected using a dedicated sensor. The power supply ripple voltage is collected by a power supply monitoring sensor, the power supply voltage is monitored in real time by a voltage sensor, the phototransistor output current is measured by a phototransistor sensor, and the ambient light intensity is monitored by a light sensor. Each collected data point should be recorded under a unified timestamp to ensure the timing consistency between data. For the collected raw illumination monitoring data, the timestamps between different acquisition channels are uniformly aligned using a multimodal timing alignment algorithm. A hardware clock synchronization method is used to correct the differences in timestamps between sensors to ensure that each data point is accurately arranged according to the sampling period, thereby avoiding data inconsistency due to timestamp errors or sampling delays. Next, the interquartile range (IQR) algorithm is used to automatically identify and remove outliers and irregular fluctuations in the original illumination monitoring data. By calculating the deviation of each data point from its quartile and using a 1.5 times IQR standard to identify outliers, data points that do not meet the standard are removed to ensure data accuracy. A local weighted regression interpolation algorithm is then used to compensate for and reconstruct missing data intervals. By weighting the neighboring data of missing data points, the missing data intervals are filled, thus ensuring data continuity. Finally, the Z-Score standardization algorithm is used to unify the dimensions and normalize the values ​​of the original illumination monitoring data. A sliding window method is used to calculate the mean and variance at each time point to ensure that dynamic changes in the data are reflected and standardized in a timely manner.

[0030] In this implementation scheme, by performing multimodal temporal alignment, outlier removal, data imputation, and normalization on the collected illumination monitoring data, the accuracy and consistency of the data can be significantly improved. This processing ensures the temporal synchronization of data from different sensors, eliminating data inconsistencies caused by sensor clock asynchrony or sampling delays. Simultaneously, the removal of outliers and irregular fluctuations effectively prevents data noise from affecting subsequent analysis, while compensation and reconstruction of missing data intervals ensure data integrity, avoiding analytical errors caused by data discontinuity. Standardization processing unifies data from different scales to the same dimension, effectively improving the comparability and accuracy of the data in further processing and analysis, providing a high-quality data foundation for subsequent brightness adjustment and fault detection.

[0031] Specifically, based on the preprocessed raw illumination monitoring data, the initial denoising data is obtained by using a bandpass filter to limit the frequency range. Further removal of power supply ripple and noise is then performed, and the denoised data is weighted and averaged to obtain effective illumination monitoring data. The specific steps are as follows: Extract the preprocessed raw illumination monitoring data and arrange it according to the sampling timestamp order to ensure that the illumination monitoring data for each data point is processed within a unified time window. Bandpass filtering is then applied to the illumination monitoring data for each sampling point sequentially, using a bandpass filter with a cutoff frequency of N Hz to M Hz to effectively filter out the acquired low-frequency and high-frequency noise, ensuring that only effective frequency components are retained in the signal, thus obtaining the initial denoising data. The values ​​of N Hz and M Hz can be set according to the actual application, where N is typically 1 Hz to 5 Hz and M is typically 50 Hz to 100 Hz to ensure that the required signal frequency band is filtered out and unnecessary frequency noise is removed. The initial denoised data is input into an FIR low-pass filter with a cutoff frequency of N Hz to remove power supply ripple and thus eliminate low-frequency noise caused by power supply interference, resulting in secondary denoised data. This secondary denoised data is then input into an IIR high-pass filter with a cutoff frequency of M Hz to remove power supply noise, yielding denoised data. After extracting the denoised data, for each data point, a weighted average is calculated using K data points before and after it. The weights of the preceding and following data points decrease with increasing distance, with points farther apart having smaller weights, and the sum of all weights is 1. The value of K ranges from 3 to 15 and is adjusted according to the specific fluctuations in the data, ensuring that the smoothed data better reflects the true illumination monitoring values, resulting in effective illumination monitoring data.

[0032] In this implementation scheme, the bandpass filter accurately filters out noise below and above a set frequency, ensuring that only meaningful frequency components of the signal are retained. Further, low-pass and high-pass filters remove power supply ripple and noise respectively, eliminating low-frequency and high-frequency noise caused by power supply interference, thus ensuring data stability and accuracy. Weighted averaging, by smoothing the data over K data points surrounding each data point, enhances data continuity and reliability, helping to more accurately reflect actual light intensity changes in subsequent analysis. This series of processing steps ensures improved accuracy and consistency of the final effective light monitoring data, providing more reliable data support for subsequent brightness adjustment and fault detection.

[0033] Specifically, the steps for assessing the current brightness fluctuation based on effective illumination monitoring data and determining whether to enter the circuit adjustment process are as follows: Extract effective illumination monitoring data and ensure the data is arranged in chronological order according to the sampling timestamps to maintain timing consistency. Compare the power supply ripple voltage with the power supply ripple threshold in real time. When the power supply ripple voltage is greater than the power supply ripple threshold, it indicates a significant power supply fluctuation, requiring the calculation of a brightness fluctuation assessment value. The brightness fluctuation assessment value is calculated by quantifying the fluctuations of the power supply ripple voltage, effective ambient light intensity, and phototransistor output current to further reflect whether the current circuit is in a stable state. Compare the brightness fluctuation assessment value with the brightness fluctuation threshold in real time. When the brightness fluctuation assessment value is less than or equal to the brightness fluctuation threshold, it indicates that the circuit is operating stably, and the current circuit should be maintained. Otherwise, it indicates that the brightness fluctuation exceeds the allowable range, requiring the entry into the circuit adjustment process. This involves adjusting the control signals to optimize circuit performance and restore stable brightness output.

[0034] In this implementation scheme, by real-time evaluation of effective illumination monitoring data and comparison of power supply ripple voltage with brightness fluctuation assessment values, it is possible to accurately determine whether the current circuit is in a stable state, thereby optimizing the timing and necessity of circuit adjustments. This process ensures a sensitive response to power supply fluctuations, enabling timely detection of excessive brightness fluctuations and triggering adjustments when necessary, avoiding unnecessary circuit adjustments and reducing system energy consumption. Simultaneously, this evaluation mechanism allows for continuous monitoring of circuit stability, ensuring that illumination output remains within the set range, thus improving system efficiency and reliability.

[0035] Specifically, the steps for calculating the brightness fluctuation assessment value are as follows: First, extract the effective ambient light intensity and effective phototransistor output current for the most recent Q sampling periods to ensure data consistency. Calculate the average effective ambient light intensity and the average effective phototransistor output current to obtain the baseline data for the current period. Next, calculate the ratio of the effective power supply ripple voltage to the power supply ripple threshold. Perform a nonlinear transformation using a power function with the power supply ripple influence coefficient as the exponent to further enhance the sensitivity to power supply ripple changes. Add a minima term to obtain the power supply ripple influence factor, which plays a crucial role in assessing the impact of power supply fluctuations on brightness fluctuations. The power supply ripple influence coefficient is calculated using a power function algorithm based on the historical effective power supply ripple voltage to the power supply ripple threshold, with a value range of 1.2 to 2.5. The minima term is a very small but non-zero positive real number used to avoid numerical instability caused by division by zero during calculation, with a value range of... arrive Unless otherwise specified, all subsequent minima referencing this definition and their ranges shall apply. Next, the absolute value of the difference between the effective ambient light intensity and its average value is calculated, and then divided by the average effective ambient light intensity plus the minima to obtain the light intensity fluctuation factor. This factor reflects the relative fluctuation of ambient light intensity changes, and the influence of dimensions is eliminated through standardization. Subsequently, the absolute value of the difference between the effective phototransistor output current and its average value is calculated, and then divided by the average effective phototransistor output current plus the minima to obtain the photocurrent fluctuation factor. This factor characterizes the fluctuation of photocurrent changes, further supporting the evaluation of circuit stability. Finally, the power supply ripple influence factor, light intensity fluctuation factor, and photocurrent fluctuation factor are multiplied sequentially to obtain the brightness fluctuation assessment value. This comprehensively considers the fluctuations of power supply ripple, ambient light intensity, and photocurrent, accurately quantifying the system's brightness stability and providing a basis for subsequent circuit adjustments.

[0036] The specific formula for calculating the brightness fluctuation assessment value is as follows:

[0037] ;

[0038] In the formula, This represents the brightness fluctuation assessment value. Indicates the effective power supply ripple voltage. Indicates the power supply ripple threshold. Indicates the effective ambient light intensity. This represents the average effective ambient light intensity. Indicates the effective output current of the phototransistor. This represents the average output current of the effective phototransistor. This represents the power supply ripple effect coefficient. Indicates a minus term.

[0039] In this embodiment, Table 1 is a data table of brightness fluctuation evaluation values, listing the brightness fluctuation evaluation values ​​and related calculation data for five sampling periods. Specifically: Sampling period 1: the effective phototransistor output current is 0.47, the effective ambient light intensity is 574.40, the power supply ripple voltage is 0.65, the power supply ripple threshold is 3.29, the average effective ambient light intensity is 763.42, the average effective phototransistor output current is 0.598, the power supply ripple influence coefficient is 1.2, and the minimum term is... The brightness fluctuation evaluation value is 0.06. Sampling period 2: The effective phototransistor output current is 0.40, the effective ambient light intensity is 454.31, the power supply ripple voltage is 4.83, the power supply ripple threshold is 2.24, the average effective ambient light intensity is 763.42, the average effective phototransistor output current is 0.598, the power supply ripple influence coefficient is 1.2, and the minimum term is... The brightness fluctuation evaluation value is 0.47. Sampling period 3: Effective phototransistor output current is 0.75, effective ambient light intensity is 846.89, power supply ripple voltage is 3.62, power supply ripple threshold is 1.17, the average effective ambient light intensity is 763.42, the average effective phototransistor output current is 0.598, the power supply ripple influence coefficient is 1.2, and the minimum term is... The brightness fluctuation evaluation value is 0.14. Sampling period 4: Effective phototransistor output current is 0.51, effective ambient light intensity is 988.31, power supply ripple voltage is 1.67, power supply ripple threshold is 2.33, average effective ambient light intensity is 763.42, average effective phototransistor output current is 0.598, power supply ripple influence coefficient is 1.2, and the minimum term is... The brightness fluctuation evaluation value is 0.07. Sampling period 5: Effective phototransistor output current is 0.86, effective ambient light intensity is 952.19, power supply ripple voltage is 1.45, power supply ripple threshold is 1.48, average effective ambient light intensity is 763.42, average effective phototransistor output current is 0.598, power supply ripple influence coefficient is 1.2, and the minimum term is... The brightness fluctuation assessment value is 0.21.

[0040] Table 1. Data Table of Brightness Fluctuation Assessment Values

[0041]

[0042] like Figure 3 As shown in the figure, the brightness fluctuation evaluation value B for five sampling periods and the circuit adjustment process entry judgment results based on the brightness fluctuation threshold are displayed. The bar chart uses different colors to distinguish the judgment status: green bars indicate that the brightness fluctuation evaluation value B for that sampling period is less than or equal to the brightness fluctuation threshold, and red bars indicate that the brightness fluctuation evaluation value B for that sampling period is greater than the brightness fluctuation threshold. The brightness fluctuation threshold line is marked with a blue dashed line in the figure, serving as the basis for determining whether to enter the circuit adjustment process. It can be seen from the figure that the brightness fluctuation evaluation value B for sampling periods 1, 3, 4, and 5 does not exceed the brightness fluctuation threshold, and the current circuit remains unchanged; the brightness fluctuation evaluation value B for sampling period 2 exceeds the brightness fluctuation threshold, and the circuit adjustment process is entered.

[0043] In this implementation scheme, brightness stability is precisely quantified by comprehensively considering fluctuations in power supply ripple, ambient light intensity, and photocurrent. By calculating and standardizing various fluctuation factors, the degree of current brightness fluctuation can be effectively assessed, providing a reliable basis for subsequent circuit adjustments. This process helps improve the accuracy and reliability of brightness adjustment, ensuring maximum stability of illumination control under various external disturbances.

[0044] Specifically, after entering the circuit adjustment process, the specific steps for extracting real-time effective illumination monitoring data to quantify LED brightness adjustment requirements are as follows: First, extract the real-time effective ambient light intensity, effective phototransistor output current, maximum effective phototransistor output current, and effective power supply ripple voltage to ensure the accuracy of data acquisition and the consistency of timing. Then, multiply the effective phototransistor output current by the effective ambient light intensity, add one, and take the natural logarithm. This nonlinear transformation effectively enhances the response capability to changes in ambient light intensity, obtaining the illumination intensity adjustment factor, which reflects the direct impact of illumination changes on LED brightness. Next, calculate the negative of the difference between the effective power supply ripple voltage and the power supply ripple threshold as an exponent, take the exponential function value of the natural constant e, and add one. This nonlinear transformation enhances the impact of power supply ripple fluctuations on brightness adjustment, thus obtaining a power supply ripple correction factor to compensate for the interference of power supply ripple on system stability. Subsequently, the ratio of the effective phototransistor output current to its maximum value is calculated to obtain the current standardization coefficient. This standardization process eliminates the influence of different data scales, allowing current variations to be fairly compared with fluctuations in other factors. Finally, the illuminance adjustment factor, power supply ripple correction factor, and current standardization coefficient are multiplied sequentially to obtain the LED brightness adjustment value. This value comprehensively considers the effects of ambient light, phototransistor output current, and power supply ripple, ensuring precise LED brightness adjustment and responsiveness to changes in the external environment.

[0045] The specific formula for calculating the LED brightness adjustment value is as follows:

[0046] ;

[0047] In the formula, Indicates the LED brightness adjustment value. Indicates the effective output current of the phototransistor. Indicates the effective ambient light intensity. Indicates the effective power supply ripple voltage. Indicates the power supply ripple threshold. This indicates the maximum effective output current of the phototransistor.

[0048] In this embodiment, by comprehensively considering ambient light intensity, phototransistor output current, and power supply ripple voltage from effective light monitoring data, the present invention effectively quantifies the LED brightness adjustment requirements. By appropriately adjusting ambient light intensity and power supply ripple using a nonlinear transformation function, the influence of these factors on brightness changes is accurately captured. Simultaneously, standardization ensures fair comparison between different variables. Through this process, the brightness adjustment value can more accurately reflect the dynamic demand of external environmental changes on LED brightness, improving the response speed and accuracy of brightness adjustment and providing a more reliable basis for stable light control.

[0049] Specifically, the steps for converting the quantization result into a control current signal to adjust the LED brightness output, and determining whether to trigger the fault detection process based on the adjustment error after circuit adjustment are as follows: A digital-to-analog converter with a resolution greater than 12 bits is used to convert the calculated LED brightness adjustment value into a corresponding control current signal, which is then input to the adjustment terminal of the constant current source circuit. The constant current source circuit adjusts the LED brightness output by adjusting the feedback resistor and the gate voltage of the MOSFET. The control interface uses current control to ensure accurate brightness adjustment. To ensure the accuracy of adjustment and the stability of the circuit, the adjustment error between the real-time transistor illumination intensity and the LED brightness adjustment value is calculated. When the adjustment error is less than the set error threshold, the adjustment is considered complete, and the current circuit remains unchanged; when the adjustment error is greater than or equal to the error threshold, the adjustment is considered a failure, a new control current signal is generated and input to the adjustment terminal of the constant current source circuit for secondary circuit adjustment. To ensure the reliability of brightness adjustment and avoid continuous unsuccessful adjustments, if W consecutive circuit adjustments fail to bring the LED brightness to the LED brightness adjustment value, the fault detection process is triggered. Here, the value of W is in the range of 3 to 5 times to ensure sufficient adjustment attempts and timely triggering of the fault detection mechanism.

[0050] In this implementation scheme, high-precision LED brightness adjustment can be achieved by precisely controlling the resolution of the digital-to-analog converter and the adjustment interface of the constant current source circuit. Simultaneously, a real-time monitoring and error feedback mechanism ensures the stability of brightness adjustment. When the circuit adjustment fails to achieve the expected brightness, a fault detection process is automatically initiated, ensuring that the system maintains high reliability and stability even after multiple adjustments. This process not only improves the accuracy of LED brightness adjustment but also effectively avoids system failures caused by repeated unsuccessful adjustments, guaranteeing stability and safety during long-term operation.

[0051] Specifically, after triggering the fault detection process, the original illumination monitoring data and effective illumination monitoring data are extracted. The effectiveness of data denoising is evaluated and quantified to determine the cause of adjustment error. The specific steps to achieve a closed loop for anti-interference brightness adjustment are as follows: After triggering the fault detection process, the original illumination monitoring data and effective illumination monitoring data are extracted, and the denoising response evaluation value is calculated. The denoising response evaluation value and the response threshold are compared in real time. When the denoising response evaluation value is greater than the response threshold, an alarm signal is automatically generated, prompting the user to check the power supply voltage and circuit components. When the denoising response evaluation value is less than or equal to the response threshold, it is determined that the data denoising is incomplete, the cutoff frequency range of the bandpass filter is adjusted, and data denoising is performed again. The specific adjustment strategy is that the cutoff frequency range of the bandpass filter will be gradually adjusted within a certain range. During each adjustment, the low-frequency cutoff frequency N and the high-frequency cutoff frequency M of the bandpass filter are increased or decreased by a fixed step size. The step size is determined based on the fluctuation range of power supply ripple and noise in the data. The frequency adjustment will be limited to the set maximum and minimum cutoff frequency range to avoid signal loss due to over-adjustment. After the frequency range of the bandpass filter is adjusted, the denoising process is repeated until the denoising response evaluation value meets the set threshold, indicating that the data has achieved the expected denoising effect, and the adjustment process ends. Subsequently, the circuit is readjusted based on new effective illumination monitoring data to ensure precise LED brightness adjustment, thereby achieving a closed-loop brightness adjustment.

[0052] In this implementation scheme, by finely adjusting the cutoff frequency range of the bandpass filter and dynamically optimizing the data denoising strategy, high-quality processing of illumination monitoring data is ensured. This strategy enables timely identification and effective adjustment of illumination data when subjected to power supply ripple and noise interference, avoiding unnecessary signal loss and improving data accuracy and reliability. By flexibly adjusting the bandpass filter parameters based on changes in the denoising response evaluation value, the system's adaptability to changes in the illumination environment is further enhanced, ensuring the timeliness and accuracy of circuit adjustments. This method effectively avoids adjustment failures caused by improper frequency range settings, guaranteeing the stability and accuracy of LED brightness adjustment under complex interference environments.

[0053] Specifically, the steps for calculating the denoising response evaluation value are as follows: First, the difference between the original ambient light intensity and the effective ambient light intensity is divided by the standard deviation of the original ambient light intensity to standardize the difference. Then, the sine value of the comparison value is taken and squared to add nonlinear processing, making the evaluation of light fluctuations more accurate. The reason for using a sine function is that its periodic variation characteristic can suppress excessive fluctuations and highlight small changes, making the evaluation results smoother when light intensity fluctuations are large, avoiding over-amplification of abrupt changes. Adding one, we obtain the light fluctuation adjustment term. This term enhances the system's sensitivity to changes in light fluctuations through nonlinear processing, especially under large fluctuations, ensuring a more accurate system response. Next, the ratio of the original power supply ripple voltage to the power supply ripple threshold is added to one and then the natural logarithm is taken to obtain the power supply ripple correction term. This term reduces the negative impact of power supply fluctuations on the overall system stability through nonlinear adjustment of the power supply ripple. Subsequently, the ratio of the effective phototransistor output current to the original maximum phototransistor output current is calculated to obtain the photocurrent influence term. This term quantifies the relative change in photocurrent, providing support for accurately evaluating the circuit's operating state. Next, the cosine of the ratio of the original maximum illumination intensity to the ambient illumination intensity is taken, and one is added to obtain the maximum illumination intensity correction term. The introduction of the cosine function is used to reduce the impact of extreme changes, especially when the ambient illumination intensity changes abruptly. The cosine function, through its smooth fluctuation characteristics, makes the system respond more smoothly to sudden and drastic changes, avoiding excessive evaluation fluctuations. Finally, the illumination fluctuation adjustment term, power supply ripple correction term, and photocurrent influence term are multiplied sequentially to form a comprehensive evaluation result. The resulting product is then divided by the maximum illumination intensity correction term to obtain the final denoising response evaluation value. Through the action of nonlinear functions such as sine and cosine, the system can better adapt to different data fluctuation characteristics, improving the accuracy of the denoising response evaluation and the robustness of the system.

[0054] The specific formula for calculating the denoising response evaluation value is as follows:

[0055] ;

[0056] In the formula, This represents the denoising response evaluation value. Indicates the original ambient light intensity. Indicates the effective ambient light intensity. Indicates the standard deviation of the original ambient light intensity. This represents the original power supply ripple voltage. Indicates the power supply ripple threshold. Indicates the effective output current of the phototransistor. This indicates the maximum output current of the original phototransistor. This represents the maximum original ambient light intensity.

[0057] In this implementation scheme, the introduction of nonlinear transformations using sine and cosine functions effectively enhances the sensitivity and stability to changes in illumination intensity, power supply ripple voltage, and photocurrent. The sine function smooths periodic variations, suppressing the impact of large fluctuations and making the assessment of illumination fluctuations more accurate, thereby enhancing adaptability to sudden fluctuations. The cosine function effectively reduces interference from extreme changes, especially when ambient illumination intensity fluctuates drastically, helping to maintain the stability of the assessment values. By comprehensively utilizing these nonlinear functions, the fluctuation levels of power supply ripple, ambient illumination intensity, and photocurrent can be accurately quantified, improving the accuracy and robustness of the denoising response assessment values, thus ensuring higher stability and reliability during brightness adjustment.

[0058] like Figure 2 As shown, the second aspect of the present invention provides an anti-interference brightness adjustment system based on photosensitive signal analysis, comprising: a data acquisition and preprocessing module, a light signal denoising module, a brightness adjustment and control module, and a fault monitoring and response module, wherein: the data acquisition and preprocessing module is used to periodically acquire raw light monitoring data, and perform multimodal temporal alignment, outlier removal, data missing interpolation, and normalization processing on the acquired data to obtain preprocessed raw light monitoring data; the light signal denoising module is used to obtain initial denoised data based on the preprocessed raw light monitoring data by using a bandpass filter to limit the frequency range, further removing power supply ripple and power supply noise, and weighting the denoised data. The system processes all data to obtain valid illumination monitoring data. The brightness adjustment and control module evaluates the current brightness fluctuation based on the valid illumination monitoring data and determines whether to enter the circuit adjustment process. Once the circuit adjustment process is entered, it extracts real-time valid illumination monitoring data to quantify the LED brightness adjustment requirements and converts the quantization result into a control current signal to adjust the LED brightness output. After the circuit adjustment is completed, it determines whether to trigger the fault detection process based on the adjustment error. The fault monitoring and response module, after triggering the fault detection process, extracts the original illumination monitoring data and valid illumination monitoring data, evaluates and quantifies the effectiveness of data denoising, determines the cause of the adjustment error, and achieves an anti-interference brightness adjustment closed loop.

[0059] like Figure 4As shown in the figure, the PCB layout design of the circuit of this invention is illustrated, with key components and connection points as shown. Specifically, GM1 is a phototransistor, responsible for converting ambient light signals into electrical signals, providing a basis for subsequent brightness adjustment. C1 and C2 are capacitors, mainly used to smooth power fluctuations, filter power noise, stabilize current output, and ensure stable circuit operation. C3 serves as an additional capacitor, further optimizing power stability and signal filtering. R1 is a resistor, used to regulate current and voltage, ensuring the safe operation of other components in the circuit. R2 is another resistor, working in conjunction with components such as C1 and C2 to optimize signal and current transmission, ensuring stable voltage in the circuit and preventing over-driving of components. D1 is a diode, preventing reverse current from damaging the circuit and ensuring unidirectional current flow. U1 is the central processing chip, responsible for processing and calculating the input signal, calculating the required LED brightness adjustment value based on ambient light intensity and power conditions, and outputting adjustment commands to the control terminal. The overall layout design is compact and reasonable, ensuring that all components cooperate with each other during operation to achieve optimal performance, thereby realizing a stable and efficient brightness adjustment function.

[0060] This implementation scheme achieves efficient noise reduction and brightness adjustment of illumination monitoring data through a precise data acquisition and processing workflow. Multi-level optimization of the acquired data ensures the accuracy and stability of the illumination data, thereby improving the precision and response speed of LED brightness adjustment. This scheme effectively addresses various interference factors such as power supply ripple, illumination intensity fluctuations, and photocurrent fluctuations, ensuring that the system can reliably adjust LED brightness in dynamically changing environments, enhancing the system's robustness and adaptability. Simultaneously, a fault detection and response mechanism ensures that anomalies can be quickly identified and adjusted accordingly, further improving the system's stability and reliability.

[0061] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0062] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to the specific implementations described. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An anti-interference brightness adjustment method based on photosensitive signal analysis, characterized in that, Includes the following steps: S1 periodically collects raw illumination monitoring data and performs multimodal temporal alignment, outlier removal, data missing imputation and normalization on the collected data to obtain preprocessed raw illumination monitoring data. S2, based on the preprocessed raw illumination monitoring data, a bandpass filter is used to limit the frequency range to obtain the initial denoising data, further remove power supply ripple and power supply noise, and perform weighted averaging on the denoised data to obtain effective illumination monitoring data. S3, based on effective illumination monitoring data, assesses the current brightness fluctuation level and determines whether to enter the circuit adjustment process; After entering the circuit adjustment process, real-time effective light monitoring data is extracted to quantify the LED brightness adjustment requirements, and the quantification results are converted into control current signals to adjust the LED brightness output. After the circuit adjustment is completed, the fault detection process is determined based on the adjustment error. S4, after triggering the fault detection process, extract the original light monitoring data and the effective light monitoring data, evaluate and quantify the effectiveness of data denoising, determine the cause of adjustment error, and realize the anti-interference brightness adjustment closed loop; After triggering the fault detection process, the original illumination monitoring data and effective illumination monitoring data are extracted. The effectiveness of data denoising is evaluated and quantified to determine the cause of the adjustment error. The specific steps to achieve the anti-interference brightness adjustment closed loop are as follows: After triggering the fault detection process, the original illumination monitoring data and effective illumination monitoring data are extracted, and the denoising response evaluation value is calculated. The denoising response evaluation value and the response threshold are compared in real time. When the denoising response evaluation value is greater than the response threshold, an alarm signal is automatically generated, prompting the user to check the power supply voltage and circuit components. When the denoising response evaluation value is less than or equal to the response threshold, it is determined that the data denoising is incomplete, the cutoff frequency range of the bandpass filter is adjusted, and the data denoising is performed again. The specific adjustment strategy is that the cutoff frequency range of the bandpass filter will be gradually adjusted within a certain range. In each adjustment, the low-frequency cutoff frequency and high-frequency cutoff frequency of the bandpass filter will be increased or decreased by a fixed step size, which is determined according to the fluctuation range of power supply ripple and noise in the data. The frequency adjustment will be limited to the set maximum and minimum cutoff frequency range to avoid signal loss due to over-adjustment. After the frequency range of the bandpass filter is adjusted, the circuit is readjusted based on the latest effective illumination monitoring data to complete the brightness adjustment closed loop.

2. The anti-interference brightness adjustment method based on photosensitive signal analysis according to claim 1, characterized in that: The specific steps for periodically collecting raw illumination monitoring data and performing multimodal temporal alignment, outlier removal, data imputation, and normalization on the collected data to obtain preprocessed raw illumination monitoring data are as follows: Set a fixed-width time window as a sampling period and periodically collect raw illumination monitoring data, which includes power supply ripple voltage, phototransistor output current, ambient light intensity and power supply voltage. For the collected raw illumination monitoring data, the timestamps between different acquisition channels are uniformly aligned using a multimodal temporal alignment algorithm, and outliers and irregular fluctuations in the raw illumination monitoring data are automatically identified and removed using a quartile spacing algorithm. The missing data intervals are compensated and reconstructed using a local weighted regression interpolation algorithm, and the raw illumination monitoring data is then processed for dimensional unification and numerical normalization using a Z-Score standardization algorithm.

3. The anti-interference brightness adjustment method based on photosensitive signal analysis according to claim 1, characterized in that: The specific steps for obtaining effective illumination monitoring data by using a bandpass filter to limit the frequency range based on the preprocessed raw illumination monitoring data are as follows: Further removal of power supply ripple and noise, and weighted averaging of the denoised data. Extract the preprocessed raw illumination monitoring data and arrange them in order of sampling timestamp. Perform bandpass filtering on the illumination monitoring data of each sampling point in sequence. Use a bandpass filter with a cutoff frequency of N Hz to M Hz to filter out noise below N Hz and above M Hz to obtain the initial denoised data. The initial denoised data is input into an FIR low-pass filter with a cutoff frequency of N Hz to remove power supply ripple and obtain the secondary denoised data. The secondary denoised data is then input into an IIR high-pass filter with a cutoff frequency of MHz to remove power supply noise and obtain denoised data. Extract the denoised data. For each data point, take the K data points before and after it and perform a weighted average. The weight of each data point before and after it decreases as the distance increases. The farther the distance, the smaller the weight. And the sum of all weight values ​​is 1, so as to obtain the effective light monitoring data.

4. The anti-interference brightness adjustment method based on photosensitive signal analysis according to claim 1, characterized in that: The specific steps for assessing the current brightness fluctuation based on effective illumination monitoring data and determining whether to proceed with the circuit adjustment process are as follows: Extract effective illumination monitoring data, compare power supply ripple voltage and power supply ripple threshold in real time. When the power supply ripple voltage is greater than the power supply ripple threshold, calculate the brightness fluctuation assessment value. Compare the brightness fluctuation assessment value and brightness fluctuation threshold in real time. When the brightness fluctuation assessment value is less than or equal to the brightness fluctuation threshold, maintain the current circuit unchanged. Otherwise, proceed with the circuit adjustment process.

5. The anti-interference brightness adjustment method based on photosensitive signal analysis according to claim 4, characterized in that: The specific steps for calculating the brightness fluctuation evaluation value are as follows: Extract the effective ambient light intensity and effective phototransistor output current of the most recent Q sampling periods, and calculate the average effective ambient light intensity and the average effective phototransistor output current respectively; calculate the power function value with the power ripple influence coefficient as the exponent for the ratio of effective power supply ripple voltage to power supply ripple threshold, and add one to obtain the power supply ripple influence factor. Calculate the absolute value of the difference between the effective ambient light intensity and the average effective ambient light intensity, and then divide it by the average effective ambient light intensity plus a minimum term to obtain the light intensity fluctuation factor; calculate the absolute value of the difference between the effective phototransistor output current and the average effective phototransistor output current, and then divide it by the average effective phototransistor output current plus a minimum term to obtain the photocurrent fluctuation factor; multiply the power supply ripple influence factor, the light intensity fluctuation factor, and the photocurrent fluctuation factor in sequence to obtain the brightness fluctuation evaluation value.

6. The anti-interference brightness adjustment method based on photosensitive signal analysis according to claim 1, characterized in that: After entering the circuit adjustment process, the specific steps for extracting real-time effective illumination monitoring data to quantify LED brightness adjustment requirements are as follows: Extract the real-time effective ambient light intensity, effective phototransistor output current, maximum effective phototransistor output current, and effective power supply ripple voltage. Add one to the product of the effective phototransistor output current and the effective ambient light intensity, and take the natural logarithm to obtain the light intensity adjustment factor. Take the negative of the difference between the effective power supply ripple voltage and the power supply ripple threshold as the exponent, take the exponential function value of the natural constant e, and add one to obtain the power supply ripple correction factor. Calculate the ratio of the effective phototransistor output current to the maximum effective phototransistor output current to obtain the current normalization coefficient; The LED brightness adjustment value is obtained by multiplying the light intensity adjustment factor, power ripple correction factor, and current normalization coefficient in sequence.

7. The anti-interference brightness adjustment method based on photosensitive signal analysis according to claim 6, characterized in that: The specific steps for converting the quantization result into a control current signal, adjusting the LED brightness output, and determining whether to trigger the fault detection process based on the adjustment error after the circuit adjustment is completed are as follows: The calculated LED brightness adjustment value is converted into a corresponding control current signal using a digital-to-analog converter, and the control current signal is input to the adjustment terminal of the constant current source circuit to adjust the feedback resistor and the gate voltage of the MOSFET. After the circuit adjustment is completed, the adjustment error of the real-time transistor light intensity and LED brightness adjustment value is calculated. When the adjustment error is less than the error threshold, the adjustment is determined to be completed and the current circuit is maintained unchanged. When the adjustment error is greater than or equal to the error threshold, the adjustment is deemed to have failed. A control current signal is regenerated and input to the adjustment terminal of the constant current source circuit for secondary circuit adjustment. If the LED brightness fails to reach the LED brightness adjustment value after W consecutive circuit adjustments, the fault detection process is triggered.

8. The anti-interference brightness adjustment method based on photosensitive signal analysis according to claim 1, characterized in that: The specific steps for calculating the denoising response evaluation value are as follows: Divide the difference between the original ambient light intensity and the effective ambient light intensity by the standard deviation of the original ambient light intensity, take the sine value of the comparison value and square it, then add one to obtain the light fluctuation adjustment term; The power ripple correction term is obtained by adding one to the ratio of the original power supply ripple voltage to the power supply ripple threshold and then taking the natural logarithm. Calculate the ratio of the effective phototransistor output current to the maximum value of the original phototransistor output current to obtain the photocurrent influence term; Take the cosine of the ratio of the original maximum light intensity to the ambient light intensity, and add one to it to obtain the maximum light intensity correction term; Multiply the illumination fluctuation adjustment term, power supply ripple correction term, and photocurrent influence term in sequence, and then divide the resulting product by the maximum illumination intensity correction term to obtain the noise reduction response evaluation value.

9. An anti-interference brightness adjustment system based on photosensitive signal analysis, employing the anti-interference brightness adjustment method based on photosensitive signal analysis as described in any one of claims 1-8, characterized in that, include: The module comprises a data acquisition and preprocessing module, a light signal denoising module, a brightness adjustment and control module, and a fault monitoring and response module, among which: The data acquisition and preprocessing module is used to periodically acquire raw illumination monitoring data and perform multimodal temporal alignment, outlier removal, data missing imputation and normalization on the acquired data to obtain preprocessed raw illumination monitoring data. The light signal denoising module is used to obtain initial denoised data based on the preprocessed original light monitoring data by using a bandpass filter to limit the frequency range, further remove power supply ripple and power supply noise, and perform weighted averaging on the denoised data to obtain effective light monitoring data. The brightness adjustment and control module is used to evaluate the current brightness fluctuation based on effective light monitoring data and determine whether to enter the circuit adjustment process. After entering the circuit adjustment process, it extracts real-time effective light monitoring data to quantify the LED brightness adjustment requirements and converts the quantification results into control current signals to adjust the LED brightness output. After the circuit adjustment is completed, it determines whether to trigger the fault detection process based on the adjustment error. The fault monitoring and response module is used to extract the original illumination monitoring data and the effective illumination monitoring data after triggering the fault detection process, evaluate and quantify the effectiveness of data denoising, determine the cause of adjustment error, and realize the anti-interference brightness adjustment closed loop.

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