Low-power-consumption brightness adaptive control method and system based on photosensitive perception

Abstract

The invention discloses a low-power-consumption brightness adaptive control method and system based on photosensitive perception, and relates to the technical field of LED brightness control. Comprising the following steps: S1, collecting brightness control data in real time, performing amplitude screening, and extracting ambient light features; s2, analyzing a relative relationship between local and extended fluctuations, and performing weighted fusion on the illumination features to form a trend illumination trajectory; s3, analyzing a change rhythm of local average illumination, identifying an illumination continuous change stage, constructing a mutation judgment index, and judging short-time false mutation; s4, target brightness is calculated, an error suppression mechanism is constructed, a brightness adjustment step length is generated, an updated duty ratio is obtained, and boundary and change rate limitation is executed; and S5, judging a working state, and executing different sampling frequency adjustment and power consumption management strategies. The problem that in a complex outdoor illumination environment, real illumination change and short-time pseudo mutation are difficult to distinguish, and consequently brightness adjustment is prone to flickering and instability is solved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of LED brightness control, in particular to a low-power brightness self-adaptive control method and system based on photosensitive sensing. BACKGROUND

[0002] With the wide application of portable electronic devices, outdoor sighting devices and low-power visual aid equipment, the use scenarios of the devices in indoor and outdoor, multi-obstruction, multi-reflection and fast light change environments are increasing. Such devices usually rely on photosensitive elements to obtain ambient light brightness information to realize adaptive adjustment of display brightness or indicator light brightness, thereby improving visual clarity, reducing visual burden and taking into account power endurance. In dynamic lighting conditions, in order to ensure the stability, timeliness and power efficiency of brightness adjustment, real-time processing and analysis of ambient light data are usually required to realize more refined brightness control.

[0003] For example, the invention patent with publication number CN111624892A discloses an intelligent furniture based on Internet of Things, relating to the technical field of furniture, especially an intelligent furniture based on Internet of Things, which comprises a wireless Internet communication module, a control processor, a dressing table bottom plate, a U-shaped frame fixed on one side of the dressing table bottom plate, the wireless Internet communication module and the control processor are fixedly connected on one side of the U-shaped frame, a table top plate is fixed on one side of the U-shaped frame, a guide hole is arranged on one side of the table top plate, a mounting plate is slidably arranged in the guide hole, a mounting groove is arranged on one side of the mounting plate, and a dressing mirror is fixedly arranged in the mounting groove. The present application has the functions of automatic light compensation, automatic adjustment of light compensation brightness, automatic lifting, automatic storage protection and dust prevention, etc. through the arrangement of the wireless Internet communication module, the control processor, the brightness adjustable light compensation lamp, the photosensitive sensor, the threaded rod, the motor, the A electric telescopic rod, the lifting device, the B electric telescopic rod and the dustproof plate.

[0004] For example, the invention patent with publication number CN118244658B discloses a smart control method, device, monitoring equipment, chip and storage medium for lighting devices, which specifically relates to a smart control method, device, monitoring equipment, chip and storage medium for lighting devices. The method comprises: acquiring the current switch state of the lighting device; when the current switch state of the lighting device is the off state, acquiring the first current ambient brightness, determining whether the opening condition is met according to the first current ambient brightness and the dynamic light-on threshold, and if the opening condition is met, switching the lighting device to the on state; after the current switch state of the lighting device is switched from the off state to the on state, acquiring the second current ambient brightness and the first current color temperature value, and determining the dynamic light-off threshold according to the second current ambient brightness and the first current color temperature value; and determining whether the closing condition is met according to the dynamic light-off threshold, and if the closing condition is met, switching the lighting device to the off state. Thus, the limitation of hardware photosensitivity is solved, and the control accuracy of the lighting device switch is improved.

[0005] However, in outdoor, forest, building shadow boundary and other scenes, the light value sampled by the photosensitive tube often has millisecond-level sudden jumps, such as hand shielding, sunlight reflection, leaf gap flickering and other short-time strong / weak light disturbances. If the LED duty cycle is directly adjusted according to the real-time sampling result, it will cause the brightness to fluctuate sharply or even flicker frequently, making the aiming field unstable, and seriously affecting the user's visual comfort and aiming accuracy. The existing system cannot reliably distinguish between "real light environment change" and "short-time pseudo-mutation", resulting in a high false trigger rate of brightness adjustment.

[0006] Therefore, in view of the above problems, there is an urgent need for a low-power consumption brightness self-adaptive control method and system based on photosensitive perception. SUMMARY

[0007] Technical problems solved In view of the deficiencies of the prior art, the present application provides a low-power consumption brightness self-adaptive control method and system based on photosensitive perception, which solves the problem that it is difficult to distinguish between real light changes and short-time pseudo-mutations in complex outdoor lighting environments, resulting in flickering and instability of brightness adjustment.

[0008] To achieve the above object, the application is implemented by the following technical solutions: the low-power consumption brightness self-adaptive control method based on photosensitive sensing, comprising the following steps: S1, collecting brightness control data in real time, performing amplitude screening on the brightness control data, constructing a local window and an extended window and extracting environmental light features to obtain a basic brightness state set; S2, analyzing the relative relationship between local and extended fluctuation degrees based on the brightness state set, performing weighted fusion on the light features to form a continuous and stable trend light trajectory, and combining historical time series to complete the inheritance and correction of the trend light; S3, analyzing the change rhythm of the local average light based on the brightness state set, and identifying the light continuous change stage, combining historical data to obtain a reference parameter set, fusing the brightness control data, the change rhythm and the light continuous change time to construct a mutation discrimination index, distinguishing between real light changes and short-time false mutations, and outputting a mutation shielding flag; S4, obtaining the target brightness according to the trend light trajectory and the mapping relationship, constructing an error suppression mechanism, and generating a brightness adjustment step length combined with the mutation discrimination index; obtaining the updated duty cycle according to the brightness adjustment step length, and performing boundary limitation and change rate limitation; S5, determining the working state according to the vibration intensity and the trend light trajectory change, and executing corresponding sampling frequency adjustment and power consumption management strategies according to different working states.

[0009] Further, the specific process of collecting brightness control data in real time, performing amplitude screening on the brightness control data, constructing a local window and an extended window and extracting environmental light features to obtain a basic brightness state set is as follows: collecting brightness control data in real time, the brightness control data including photosensitive original value, vibration count, LED drive duty cycle and time stamp; performing amplitude checking based on the difference value of the photosensitive original value at adjacent time points and the standard deviation of the photosensitive original value in the sliding window, if the difference value exceeds the maximum light change amplitude threshold and the standard deviation is less than the fluctuation threshold, marking the sampling point as an abnormal point and removing it; constructing a local sampling window and an extended sampling window based on fixed sampling numbers N and M respectively; calculating the average value and the standard deviation of the photosensitive original value in the local sampling window to obtain the local average light value and the local fluctuation degree; based on the extended sampling window, calculating the average value and the standard deviation of the photosensitive original value to obtain the extended average light value and the extended fluctuation degree; constructing a brightness control database, and writing the brightness control data, the local average light value, the local fluctuation degree, the extended average light value and the extended fluctuation degree into the brightness control database.

[0010] Further, based on the relative relationship between the local and extended fluctuation degrees of the brightness state set, the illumination features are weighted and fused to form a continuous and stable trend illumination trajectory. The specific process of inheritance and correction of the trend illumination combined with historical time sequence is as follows: the local fluctuation degree and the extended fluctuation degree are read and summed, and the sum is added to a minimum constant value to obtain a smooth normalized reference value; the extended fluctuation degree is divided by the smooth normalized reference value to obtain a trend illumination weighted value; a constant is subtracted from the trend illumination weighted value to obtain a local average illumination weighted value; the trend illumination weighted value is multiplied by the trend illumination value at the previous moment to obtain an extended weighted inheritance item, and if it is initial calculation, the trend illumination value at the previous moment is assigned as the extended average illumination value; the local average illumination weighted value is multiplied by the current local average illumination value to obtain a local weighted update item; the extended weighted inheritance item and the local weighted update item are added to obtain the trend illumination value at the current moment; the trend illumination value is calculated in real time, boundary check and time synchronization are performed on the trend illumination value sequence, and the trend illumination value sequence is written into the brightness control database.

[0011] Further, the change rhythm of the local average illumination is analyzed according to the brightness state set, and the illumination continuous change phase is identified. The specific process of obtaining the reference parameter set combined with historical data is as follows: the sequence of continuous local average illumination values is read, the absolute change rate of the local average illumination values at adjacent moments is calculated based on the time stamp by difference method, and the difference between the current local average illumination value and the local average illumination value at the previous moment is calculated at each sampling moment. If the difference exceeds the minimum effective change threshold, it is considered that the illumination change is in the continuous phase, the time stamp difference of the continuous continuous phase is formed by tracing back from the current moment and accumulating, and the accumulated continuous time is taken as the illumination change duration. Based on the sliding time window, the sequence of local average illumination value absolute change rate, the sequence of vibration count and the sequence of illumination change duration are read, and the median is selected as the local average illumination value change rate reference value, the vibration count reference value and the illumination change duration reference value respectively.

[0012] Further, the specific process of constructing the mutation discrimination index by fusing the brightness control data, the change rhythm and the illumination continuous change time is as follows: the current illumination change duration is divided by the illumination change duration reference value, and the reciprocal is taken as the exponential power to perform natural exponential operation to obtain a duration attenuation item; the current local average illumination value absolute change rate is divided by the local average illumination value change rate reference value to obtain a normalized change rate item; the current vibration count is divided by the vibration count reference value to obtain a normalized vibration intensity item; the duration attenuation item, the normalized change rate item and the normalized vibration intensity item are multiplied to obtain an illumination pseudo-mutation discrimination value.

[0013] Further, the specific process of distinguishing real light changes from short-time pseudo mutations and outputting mutation shielding flags is as follows: the calculated light pseudo mutation discrimination value is limited in the interval of zero to the maximum boundary threshold; at the same time, the average value of the light pseudo mutation discrimination value is calculated based on the sliding time window, and time smoothing processing is performed on the pseudo mutation discrimination value sequence; the smoothed pseudo mutation discrimination value is compared with the mutation threshold value; if the pseudo mutation discrimination value is less than the mutation threshold value, the brightness adjustment process is entered; if the pseudo mutation discrimination value is greater than or equal to the mutation threshold value, the pseudo mutation shielding flag is generated, the shielding instruction is output, the brightness adjustment process is suspended, and the brightness output at the last moment is maintained; the original light pseudo mutation discrimination value, the smoothed pseudo mutation discrimination value and the pseudo mutation shielding flag are written into the brightness control database.

[0014] Further, the specific process of obtaining the target brightness according to the trend light trajectory and the mapping relationship, constructing the error suppression mechanism, and generating the brightness adjustment step combined with the mutation discrimination index is as follows: the current trend light value and duty cycle are obtained, and the brightness adjustment process is performed; a fixed light brightness mapping function is obtained, the current trend light value is mapped to the target duty cycle, and the difference between the target duty cycle and the current moment duty cycle is calculated to obtain the duty cycle error; based on the sliding time window, the median of the historical duty cycle error is selected as the typical adjustment scale value; the smoothed pseudo mutation discrimination value is read, and the pseudo mutation discrimination value is compressed by using the Sigmoid function to obtain the pseudo mutation compressed discrimination value; the pseudo mutation adjustment term is obtained by dividing the typical adjustment scale value by the sum of the constant one and the pseudo mutation compressed discrimination value; the normalized error suppression term is obtained by dividing the current duty cycle error by the typical adjustment scale value and performing hyperbolic tangent operation; the brightness update step value is obtained by multiplying the typical adjustment scale value and the normalized error suppression term.

[0015] Further, the specific process of obtaining the updated duty cycle according to the brightness adjustment step and performing boundary limitation and change rate limitation is as follows: the brightness update step value and the current moment duty cycle are added to obtain the updated duty cycle, the updated duty cycle is written into the LED driving unit as a new brightness adjustment instruction to adjust the actual luminous brightness at the current moment; the boundary limitation is performed on the updated duty cycle, and the updated duty cycle is compared with the highest duty cycle limitation threshold and the lowest duty cycle limitation threshold to be limited in the interval of the lowest duty cycle limitation threshold and the highest duty cycle limitation threshold; the change rate limitation is performed on the updated duty cycle, and if the absolute difference between the updated duty cycle and the current duty cycle is greater than the maximum duty cycle change threshold, the updated duty cycle is limited in the range between the current duty cycle minus the maximum duty cycle change threshold and the current duty cycle plus the maximum duty cycle change threshold; the updated duty cycle is written into the brightness control database, and the brightness adjustment completion flag is generated.

[0016] Further, the working state is determined according to the vibration intensity and the trend light track change, and the specific process of executing the corresponding sampling frequency adjustment and power consumption management strategy according to different working states is as follows: after the brightness adjustment is completed, the vibration count and the trend light value change amplitude are read, and the state is detected, wherein the trend light value change amplitude is obtained by calculating the absolute difference of adjacent trend light values; if the vibration count is lower than the vibration threshold and the trend light value change amplitude is lower than the trend light change threshold, it is determined that the state is static, the photosensitive original value sampling frequency is reduced, and the MCU enters low-power operation; if the vibration count is greater than or equal to the vibration threshold or the trend light value change amplitude is greater than or equal to the trend light change threshold, it is determined that the state is a use state, and the photosensitive original value sampling frequency is increased.

[0017] The second aspect of the present application provides a low-power brightness self-adaptive control system based on photosensitive sensing, comprising: a brightness data acquisition and processing module, used for real-time acquisition of brightness control data, amplitude screening of the brightness control data, construction of a local window and an extended window, extraction of ambient light features, and obtaining of a basic brightness state set; a trend light smoothing calculation module, used for analyzing the relative relationship between local and extended fluctuation degrees based on the brightness state set, weighting and fusing the light features, forming a continuous and stable trend light track, and combining historical time sequences to complete inheritance and correction of the trend light; a light pseudo-mutation detection module, used for analyzing the change rhythm of local average light based on the brightness state set, identifying a light continuous change stage, obtaining a reference parameter set combined with historical data, fusing the brightness control data, the change rhythm and the light continuous change time to construct a mutation discrimination index, distinguishing between real light change and short-time pseudo-mutation, and outputting a mutation shielding flag; a flicker-free brightness adjustment module, used for obtaining a target brightness according to the trend light track and a mapping relationship, constructing an error suppression mechanism, and generating a brightness adjustment step combined with the mutation discrimination index; obtaining an updated duty cycle according to the brightness adjustment step, and executing boundary limitation and change rate limitation; and a low-power state management module, used for determining the working state according to the vibration intensity and the trend light track change, and executing the corresponding sampling frequency adjustment and power consumption management strategy according to different working states.

[0018] The present application has the following beneficial effects: (1) The present application can stably extract real ambient light features by constructing a multi-scale fusion mechanism of local and extended windows, combining amplitude screening and fluctuation degree analysis, effectively suppressing photosensitive peak noise caused by transient disturbances such as hand occlusion, reflection spots, leaf gaps, etc., and improving the reliability of ambient light recognition from the root.

[0019] (2) In this invention, by adopting an adaptive weighted strategy that combines local illumination with extended illumination and by combining historical trend inheritance, the trend illumination trajectory remains smooth and continuous during the transition of illumination changes, avoiding brightness adjustment jitter caused by single abnormal sampling or short-cycle fluctuations, and providing a stable basis for subsequent dimming logic.

[0020] (3) By integrating the light change rhythm, duration characteristics and vibration intensity, and constructing an exponential decay term and a multidimensional normalized discrimination index, this invention can accurately identify short-term pseudo-mutations and achieve brightness shielding in complex outdoor scenes, effectively reduce the brightness false trigger rate, and avoid obvious flickering of LED brightness due to instantaneous impact.

[0021] (4) In this invention, after the brightness adjustment closed loop, the current working state is automatically determined according to the vibration intensity and trend light change amplitude, and the sampling frequency and MCU power consumption mode are dynamically adjusted so that the device reduces the photosensitive sampling rate and average power consumption in the static state and maintains a fast brightness response in the use state, thus achieving an effective balance between brightness performance and energy consumption.

[0022] Of course, any product implementing this invention does not necessarily need to achieve all of the advantages described above at the same time. Attached Figure Description

[0023] Figure 1 The flowchart shows a low-power brightness adaptive control method based on photosensitivity. Figure 2 This is a structural diagram of a low-power brightness adaptive control system based on photosensitivity. Figure 3 This is a time-series trend chart of the pseudo-mutation discriminant value for illumination. Figure 4 Radar chart of three factors for pseudo-mutation of illumination; Figure 5 This is a schematic diagram of the PCB structure of an electronic circuit module. Detailed Implementation

[0024] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. As those skilled in the art will understand, 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.

[0025] Please see Figures 1-5 This invention provides a technical solution: a low-power brightness adaptive control method and system based on photosensitivity, such as... Figure 1As shown, the process includes the following steps: S1, real-time acquisition of brightness control data, amplitude screening of the brightness control data, construction of local and extended windows, extraction of ambient light features, and obtaining a basic brightness state set; S2, analysis of the relative relationship between local and extended fluctuations based on the brightness state set, weighted fusion of illumination features to form a continuous and stable trend illumination trajectory, and inheritance and correction of trend illumination by combining historical time series; S3, analysis of the change rhythm of local average illumination based on the brightness state set, identification of the continuous change stage of illumination, obtaining a reference parameter set by combining historical data, fusion of brightness control data, change rhythm and continuous change time of illumination to construct a mutation discrimination index, distinguishing between real illumination changes and short-term pseudo-mutations, and outputting a mutation shielding flag; S4, obtaining the target brightness based on the trend illumination trajectory and mapping relationship, constructing an error suppression mechanism, and generating a brightness adjustment step size by combining the mutation discrimination index; obtaining the updated duty cycle based on the brightness adjustment step size, and implementing boundary constraints and change rate constraints; S5, determining the working state based on vibration intensity and trend illumination trajectory changes, and implementing corresponding sampling frequency adjustment and power consumption management strategies according to different working states.

[0026] Specifically, the process of acquiring brightness control data in real time, screening the brightness control data for amplitude, constructing local and extended windows, and extracting ambient light features to obtain the basic brightness state set is as follows: Brightness control data is acquired in real time, including: original photosensitive value, vibration count, LED driver duty cycle, and timestamp. The original photosensitive value is continuously output by the photosensitive sensor at a fixed sampling frequency; the vibration count is obtained by accumulating the number of triggers by the MEMS vibration sensor in each sampling cycle; the LED driver duty cycle is read in real time by the brightness control chip; and the timestamp is automatically generated by the main control module during data writing, ensuring that all data have consistent time-series attributes for easy subsequent analysis and processing. All data is acquired and written by the same MCU main control unit within a unified sampling cycle, and is strictly aligned on the time axis. Amplitude checks are performed based on the difference between the original photosensor values ​​at adjacent time points and the standard deviation of the original photosensor values ​​within the sliding window. If the difference exceeds the maximum illumination change amplitude threshold and the standard deviation is less than the fluctuation threshold, the sampling point is marked as an anomaly and removed. The maximum illumination change amplitude threshold is adaptively set according to the physical response range of the photosensitive device and the maximum rate of change of ambient light under normal conditions. This is used to avoid interference from non-realistic illumination jumps caused by sudden occlusion, light spot sweeping, or sensor jitter in the feature extraction process. The standard deviation is used to characterize the overall stability of illumination fluctuations within the sliding window. When the standard deviation is at a small level, it indicates that the original photosensor values ​​within the window are generally stable. If adjacent sampling points show a sudden increase or decrease, it can be judged as a local instantaneous jump rather than a real illumination change. The fluctuation threshold can be determined based on the statistics of historical original photosensor values ​​to ensure that real ambient light changes can be effectively distinguished from isolated anomalies caused by instantaneous noise, occlusion edges, and sensor jitter. The removed sampling points do not participate in subsequent window statistics to ensure the stability and repeatability of brightness state features. A local sampling window and an extended sampling window are constructed based on fixed sampling numbers N and M, respectively. N is the length of the local sampling window, used to characterize illumination changes within a short timescale, preferably with 5 to 20 sampling points. M is the length of the extended sampling window, used to construct an ambient light baseline over a long timescale, preferably with 50 to 200 sampling points to cover a wider range of historical illumination samples. Both windows use a sliding update method, meaning the window content automatically moves forward as new sampling points arrive, achieving real-time continuous monitoring. When the sampling frequency is high, the amount of data collected per unit time increases, and the values ​​of N and M can be appropriately increased to ensure that the local and extended windows cover a sufficient timescale, thereby obtaining more stable ambient light characteristics. When the sampling frequency is low, to avoid excessive smoothing of illumination changes due to an excessively long window time span, the values ​​of N and M are correspondingly reduced to ensure the response speed of the brightness adjustment process.Furthermore, in dynamic scenes with rapidly changing illumination, such as outdoor strong light shading, light spot sweeping, and handheld mobile environments, a smaller N is preferred to improve the sensitivity of the local window to instantaneous changes; in indoor scenes with relatively stable illumination, a larger M is preferred to construct a more robust ambient light baseline. The mean and standard deviation of the raw photosensitivity values ​​within the local sampling window are calculated to obtain the local average illumination value and local volatility. Based on the extended sampling window, the mean and standard deviation of the raw photosensitivity values ​​are calculated to obtain the extended average illumination value and extended volatility. The local average illumination value characterizes the current illumination level, while the local volatility reflects the drastic degree of illumination change over a short period. The extended average illumination value can serve as the baseline illumination level for the brightness adjustment algorithm, and the extended volatility can serve as an important indicator for judging whether the environment is stable in the long term. The combined use of these two statistical scales can effectively distinguish between normal illumination changes, instantaneous pseudo-abrupt changes, and continuous trend changes. A brightness control database is constructed, and brightness control data, local average illumination values, local volatility, extended average illumination values, and extended volatility are written into the brightness control database. The brightness control database records all processed valid data in chronological order, which is used for online inference and historical backtracking analysis of the subsequent brightness adjustment model.

[0027] like Figure 5 The diagram shows a schematic of the PCB structure of the electronic circuit module. It displays the front and back wiring views of the circuit board. The top two views are EDA wiring views, showing signal traces, pad distribution, and component layout; the bottom two views are 3D appearance views, showing the component mounting structure in its actual packaged state. GM1 is the signal interface location of the photosensitive device, near which are filtering and voltage divider circuits such as R1, C1, and C2, used to achieve stable acquisition of the original photosensitive value. U1 is the main control MCU, responsible for executing the algorithms of various modules in this invention, including local and extended window feature extraction, trend illumination calculation, pseudo-mutation identification, brightness step generation, and duty cycle update. The writing of the LED duty cycle is completed by the internal PWM unit of U1. The vibration counting related devices are located near GM1 in the diagram, used to collect the number of vibration triggers. The CR1632 battery holder in the lower 3D diagram is the power supply unit for the entire device. Combined with the adaptive sampling frequency adjustment and low-power operation strategy of the MCU in this invention, it can significantly extend battery life, reducing the sampling frequency in the static state and restoring the rated sampling frequency in the use state.

[0028] In this implementation scheme, by real-time acquisition of raw photosensitivity values, vibration counts, duty cycles, and timestamps, combined with steps such as amplitude screening of adjacent sampling points, adaptive threshold removal of outliers, and local and extended dual-window feature extraction, the current illumination level, short-term fluctuation characteristics, and long-term ambient light trends can be obtained simultaneously at different time scales. This effectively avoids interference from pseudo-mutations such as sudden occlusion, light spot sweeping, or sensor jitter on brightness adjustment, ensuring the stability, continuity, and repeatability of illumination feature description. By writing the above multi-scale statistics into the brightness control database, not only can reliable real-time illumination status be provided for adaptive brightness adjustment, but it also supports historical backtracking, trend analysis, and strategy optimization of subsequent algorithms, making the overall brightness control process more robust, accurate, and practically feasible.

[0029] Specifically, based on the analysis of the relative relationship between local and extended volatility based on the brightness state set, the illumination features are weighted and fused to form a continuous and stable trend illumination trajectory. The process of inheriting and correcting the trend illumination, combined with historical time series, is as follows: Local and extended volatility are read and summed. The sum comprehensively reflects the current illumination fluctuation level. The sum is added to a minimum constant value to obtain a smoothed and normalized baseline value. The extended volatility is divided by the smoothed and normalized baseline value to obtain the trend illumination weighted value. The trend illumination weighted value measures the stability of ambient light over a longer time scale. When the extended volatility is large, the weighted value increases, giving the trend term a higher weight, which helps suppress noise fluctuations caused by local abrupt changes. The trend illumination weighted value is subtracted from the constant to obtain the local average illumination weighted value. The local average illumination weighted value reflects the contribution of current short-term illumination changes and complements the trend illumination weighted value, ensuring overall weight normalization and making the fusion process physically interpretable. Multiplying the trend illumination weighted value by the previous trend illumination value yields the extended weighted inheritance term. If it's an initial calculation, the previous trend illumination value is assigned the extended average illumination value. The previous trend illumination value is read from the brightness control database according to the timestamp index. If there is no historical value in the database, the extended average illumination value is used as the initial estimate to ensure the algorithm can execute normally in cold start scenarios. Multiplying the local average illumination weighted value by the current local average illumination value yields the local weighted update term. The local weighted update term is used to inject the latest environmental light sensitivity changes into the trend calculation, enabling the trend trajectory to respond to real changes in a timely manner and avoiding lag caused by over-smoothing. Adding the extended weighted inheritance term and the local weighted update term yields the current trend illumination value. The trend illumination value, as the fusion result, combines short-term sensitivity and long-term smoothness, effectively filtering out pseudo-abrupt factors caused by jitter and occlusion, providing a stable and reliable reference illumination signal for subsequent automatic brightness adjustment. The trend illumination value is calculated in real time, and boundary checks and time synchronization are performed on the trend illumination value sequence before being written to the brightness control database. Boundary checks are used to avoid the generation of abnormal trend illumination values ​​due to abnormal inputs and extreme conditions. Time synchronization is used to ensure that the trend illumination trajectory is aligned with other monitoring data on the same timeline. Database storage ensures that all trend illumination values ​​can be used for subsequent adjustment strategies, algorithm optimization, and offline analysis.

[0030] The specific formula for the trend illumination value is as follows: ; In the formula, This represents the trend illumination value, which serves as the ambient light estimate after jitter reduction and is used to drive subsequent brightness mapping. This represents the extended volatility, reflecting the degree of light fluctuation over a longer time scale, equivalent to the long-window volatility intensity. It represents the local volatility, reflecting the intensity of instantaneous fluctuations within a short time window; It represents the trend illumination value at the previous moment, providing temporal inertia to ensure that the trend illumination does not change abruptly between adjacent samples; This represents the local average illumination value, which is an instantaneous ambient light estimate based on the current local sampling window and mainly reflects the current illumination level. This represents a very small constant value to prevent calculation errors caused by a denominator of zero. It can also introduce a minimum smoothing scale in scenarios with almost no fluctuations to avoid trend value jitter caused by weights being too extreme. The preferred value is 0.01.

[0031] In this implementation scheme, dual-scale illumination features are constructed by simultaneously utilizing local and extended volatility to collaboratively characterize short-term ambient light changes and long-term illumination trends. Adaptive weighted fusion is used to form a smooth and real-time trend illumination trajectory. Historical trend values ​​and extended average illumination values ​​are introduced into the trend inheritance, enabling the algorithm to maintain stability in both cold start and normal operation scenarios. Simultaneously, a local weighted update term ensures that the trend trajectory responds promptly to real illumination changes, avoiding adjustment lag caused by excessive smoothing. Boundary checks and time synchronization mechanisms further ensure that the trend illumination sequence is numerically controllable and temporally aligned, providing the brightness adjustment module with a continuous, stable, and physically meaningful reference illumination signal, thereby significantly improving the reliability, anti-interference capability, and practical applicability of automatic brightness control.

[0032] Specifically, the process of analyzing the rhythm of local average illumination changes based on the brightness state set, identifying the continuous illumination change phase, and obtaining a reference parameter set by combining historical data is as follows: A continuous sequence of local average illumination values ​​is read; based on timestamps, the absolute rate of change of local average illumination values ​​at adjacent times is calculated using the difference method. The difference operation is implemented by sequentially reading adjacent sampling points according to the timestamp order. The timestamps are automatically generated by the main control module to ensure the accuracy of the sampling interval, making the rate of change calculation repeatable and engineering feasible. Simultaneously, at each sampling moment, the difference between the current local average illumination value and the previous local average illumination value is calculated to characterize the single-step change amplitude, effectively identifying significant change events caused by occlusion, light spot sweeping, and rapid scene illumination switching. If the difference exceeds the minimum effective change threshold, the illumination change is considered to be in a continuous phase. The minimum effective change threshold is determined based on the noise level of the photosensitive device. The ambient light fluctuation range is adaptively set, preferably 1 to 3 times the local fluctuation range, to avoid sensor noise and minute disturbances being misjudged as changes in illumination. The duration is formed by tracing back from the current moment and accumulating the timestamp differences of consecutive continuous stages. The tracing process is achieved by checking sampling points that continuously meet the threshold conditions within the local buffer. The timestamp difference between each sampling point and the previous sampling point is calculated, and the consecutive time differences are accumulated. The sum is the duration of illumination change, ensuring that the duration can accurately reflect the true continuity of illumination change, rather than instantaneous fluctuations. The accumulated duration is used as the duration of illumination change. Based on a sliding time window, the local average illumination value absolute change rate sequence, vibration count sequence, and illumination change duration sequence are read, and the median is selected as the benchmark value for the local average illumination value change rate, vibration count, and illumination change duration, respectively. The sliding time window is preferably set to 2 to 5 times the length of the local sampling window, but not exceeding the length of the extended sampling window. The sliding time window updates forward by a fixed length, which is used to obtain robust reference parameter statistical results while ensuring real-time performance. Selecting the median as the reference value can enhance the ability to resist outliers. Moreover, the median as a statistic does not depend on the maximum or minimum value in the sorted sequence, and is less susceptible to the influence of short-term sudden occlusion, jitter noise, or vibration peaks than the mean. The output is determined by the data at the center position of the sequence, which can effectively reduce the pulling effect of isolated outliers on the overall benchmark value, thus having stronger robustness and anti-interference ability. A stable set of reference parameters can still be obtained under strong interference scenarios, providing a reliable basis for subsequent identification of illumination changes.

[0033] In this implementation scheme, a detailed analysis of the rhythmic changes in the local average illumination sequence is performed. This analysis, combined with a minimum effective change threshold, identifies continuous illumination change stages, and the duration of illumination changes is accurately obtained using a continuous timestamp accumulation method. Simultaneously, robust statistics on the rate of change, vibration counts, and duration are performed based on a sliding time window, and the median is extracted as a reference parameter set. This ensures reliable characterization of real illumination changes even under multi-source perturbation conditions. This method effectively distinguishes between sensor noise, transient occlusion, and real ambient light trends, providing a stable, interference-resistant, and repeatable parameter basis for identifying pseudo-amplitude illumination changes and subsequent brightness adjustment, significantly improving environmental adaptability and the accuracy of brightness control.

[0034] Specifically, the process of constructing abrupt change discrimination index by integrating brightness control data, change rhythm, and duration of illumination change is as follows: Divide the current duration of illumination change by the baseline value of illumination change duration, and take the opposite as the exponent for natural exponential calculation to obtain the duration decay term; the duration of illumination change reflects whether the illumination change is stable or merely a transient disturbance; the baseline value of illumination change duration is obtained through the median of a sliding time window, possessing statistical robustness, allowing the exponential decay term to adapt to the illumination change rhythm under different environments. A smaller duration decay term indicates a higher probability of a short-term pseudo-abrupt change, while a larger one indicates stronger persistence of the change; divide the absolute change rate of the current local average illumination value by the baseline value of the local average illumination value change rate to obtain the normalized change rate term; the normalized change rate term is used to quantify the severity of the current illumination change, with the denominator being the baseline change rate, which can account for sampling noise and... The variation amplitude under different light intensities is normalized to make the rate of change comparable under different light levels, thereby enhancing the generalization of the mutation index under different environmental conditions. The normalized vibration intensity term is obtained by dividing the current vibration count by the vibration count benchmark value. The vibration count is used to determine whether the light change is caused by non-light factors such as hand tremors or equipment vibration. Normalization can enhance the sensitivity to abnormal vibrations and avoid misjudging light jumps as real light changes when equipment shakes. The light pseudo-mutation discrimination value is obtained by multiplying the duration decay term, the normalized rate of change term, and the normalized vibration intensity term. The three terms are independent and complementary, and can simultaneously determine whether the current light change is a pseudo-mutation from three dimensions: change duration, change amplitude, and external vibration influence. This improves the accuracy and anti-interference ability of mutation identification, and makes the final mutation discrimination result stable, interpretable, and engineering feasible.

[0035] The specific formula for the illumination pseudo-mutation discriminant value is as follows: ; In the formula, This represents the illumination pseudo-mutation discrimination value, which is used to quantitatively evaluate whether the current illumination change is caused by pseudo-mutations due to factors such as short-term occlusion or jitter. The larger the value, the higher the suspicion of pseudo-mutation. It indicates the duration of light change, measures whether the light change is continuous, and is a key dimension that distinguishes between short-term flicker and continuous environmental change. It represents a baseline value for the duration of light change, providing a reference scale for the duration of light change under normal conditions, and is used for normalization; This represents the local average illuminance value; This represents the local average illumination value at the previous moment, used to calculate the instantaneous rate of change of illumination, providing a key dimension of the speed of illumination change. This represents the timestamp interval, converting the difference between adjacent samples into a rate of change; This represents the baseline value for the rate of change of local average illuminance, normalizes the rate of change of illuminance, and makes the rate of change comparable under different environments. It indicates the vibration count, reflecting whether there is physical shaking or movement, and is used to distinguish between changes in ambient light and false flickering caused by vibration; This represents the baseline value for vibration counting, normalizes vibration counts, and achieves a unified scale across different devices and scenarios.

[0036] In this embodiment, Table 1 is a data table of illumination pseudo-abrupt change discrimination values. The timestamp interval is set to 0.1, the local average illumination value change rate baseline is 80, the vibration count baseline is 5, and the illumination change duration baseline is 0.5. The table records in detail the current local average illumination value, the previous local average illumination value, the illumination change duration, the vibration count, the duration decay term, the normalized change rate term, the normalized vibration intensity term, and the illumination pseudo-abrupt change discrimination value corresponding to five time points. Among them, the current local average illumination value corresponding to time 1 is 500, and the previous local average illumination value is 490. The duration of the illumination change is 0.1, the vibration count is 1, the duration decay term is 0.8187, the normalized rate of change term is 1.250, the normalized vibration intensity term is 0.2, and the illumination pseudo-abrupt change discrimination value is 0.2047; the current local average illumination value at time 2 is 508, the previous local average illumination value is 500, the duration of the illumination change is 0.2, the vibration count is 2, the duration decay term is 0.6703, the normalized rate of change term is 1.000, and the normalized vibration intensity term is 0.4. The false abrupt change discrimination value is 0.2681; the current local average illumination value at time 3 is 560, the previous local average illumination value is 508, the duration of illumination change is 0.3, the vibration count is 9, the duration decay term is 0.5488, the normalized rate of change term is 5.200, the normalized vibration intensity term is 1.8, and the false abrupt change discrimination value is 6.4211; the current local average illumination value at time 4 is 558, the previous local average illumination value is 560, and the duration of illumination change is 0.0. The vibration count is 2, the duration decay term is 1.0000, the normalized rate of change term is 0.250, the normalized vibration intensity term is 0.4, and the illumination pseudo-abrupt value is 0.1000; the current local average illumination value at time 5 is 575, the previous local average illumination value is 558, the illumination change duration is 0.1, the vibration count is 3, the duration decay term is 0.8187, the normalized rate of change term is 2.125, the normalized vibration intensity term is 0.6, and the illumination pseudo-abrupt value is 1.0439.

[0037] Table 1 Data on pseudomutation discriminant values ​​under illumination

[0038] like Figure 3 The figure shows the time-series trend of the illumination pseudo-mutation discrimination value. It illustrates the change trend of the illumination pseudo-mutation discrimination value over time, with the horizontal axis representing the time number and the vertical axis representing the illumination pseudo-mutation discrimination value. (This is in conjunction with Table 1 and...) Figure 3It can be seen that the illumination pseudo-mutation discrimination value remained at a low level at times 1 and 2, with small illumination changes and low vibration counts, indicating stable illumination changes and weak disturbances, which were identified as normal illumination changes, thus enabling stable brightness adjustment. When time 3 was reached, the local average illumination rose sharply from 508 to 560, while the vibration count reached 9 and the illumination change lasted for a long time, resulting in a significant peak in the illumination pseudo-mutation discrimination value. It was determined that this change was most likely caused by short-term disturbances such as occlusion and jitter, indicating a strong short-term abnormal disturbance, triggering the pseudo-mutation shielding mechanism to prevent brightness adjustment from being triggered at that moment, which would cause LED flickering. At time 4, the illumination pseudo-mutation discrimination value dropped to near zero again, indicating that the environment returned to a stable state. At time 5, there was a slight rebound, but the overall disturbance level was still limited, indicating that there was a slight disturbance but it did not reach an abnormal level.

[0039] like Figure 4 The image shows a radar chart of three factors contributing to pseudo-amplitude changes in illumination. It displays a radar chart of the illumination pseudo-amplitude discrimination index based on three factors: duration decay, normalized rate of change, and normalized vibration intensity, used to visually compare the differences in illumination abrupt change characteristics at five time points. (This is in conjunction with Table 1 and...) Figure 4 As can be seen, the radar area is largest at time 3, especially showing a significant peak in the normalized rate of change term, and the vibration intensity term is also significantly higher than at other times, indicating that a rapid increase in illumination and strong vibration occurred simultaneously, representing the most typical short-term pseudo-abrupt change scenario, hence the highest pseudo-abrupt change discrimination value. In contrast, the three indicators at times 1 and 2 are all at lower levels, with small rates of change and weak vibration, exhibiting characteristics of natural illumination changes, hence the lowest corresponding pseudo-abrupt change discrimination value. At time 4, the rate of change is almost zero, the duration decays to 1.0, and the vibration intensity is also low, indicating stable ambient light without abrupt changes, hence the lowest pseudo-abrupt change discrimination value. At time 5, the three indicator values ​​are slightly improved, with both vibration and illumination changes present but not drastic, hence the pseudo-abrupt change discrimination value is at a moderate level. Overall, the radar chart clearly presents the differences in illumination abrupt change characteristics at the five times, distinguishing between real illumination changes and short-term pseudo-abrupt changes, providing an intuitive basis for subsequent anti-flicker control of brightness adjustment.

[0040] In this implementation scheme, by uniformly quantifying three dimensions—duration of illumination change, local average illumination change rate, and vibration intensity—a mutation discrimination index is constructed that can simultaneously reflect the persistence of change, the severity of change, and the level of external interference. This index can reliably distinguish between real illumination changes and short-term pseudo-mutations, thereby avoiding false triggering caused by factors such as occlusion, reflection, and jitter. The normalization and exponential decay structure give the index adaptability and cross-scene stability, maintaining consistent judgment results even under different ambient light intensities, device postures, and noise conditions. The overall method has advantages such as low computational cost, strong real-time performance, and high anti-interference capability, providing an accurate and stable illumination judgment basis for subsequent brightness adjustment, effectively improving the reliability of brightness control and user experience.

[0041] Specifically, the process of distinguishing between real illumination changes and short-term pseudo-mutations, and outputting a mutation masking flag, is as follows: The calculated illumination pseudo-mutation discrimination value is limited to the range of zero to the maximum boundary threshold. The maximum boundary threshold is adaptively set according to the acceptable maximum pseudo-mutation intensity, preferably a value between 10 and 20, to prevent oversaturation output caused by abnormal calculations and ensure that the discrimination value changes within a controllable range. Simultaneously, the average value of the illumination pseudo-mutation discrimination value is calculated based on a sliding time window, and time smoothing is performed on the pseudo-mutation discrimination value sequence. The smoothing is achieved by averaging the illumination pseudo-mutation discrimination values ​​within the window range, suppressing the instantaneous shift caused by single-point noise in the pseudo-mutation judgment, making the discrimination more stable and practical for engineering applications. The sliding time window adopts a fixed length design and slides forward and updates over time to ensure the real-time nature of the discrimination process. The smoothed pseudo-mutation discrimination value is compared with the mutation threshold: if the pseudo-mutation discrimination value is less than the mutation threshold, the brightness adjustment process begins; if the pseudo-mutation discrimination value is greater than or equal to the mutation threshold, a pseudo-mutation shielding flag is generated, a shielding command is output, the brightness adjustment process is paused, and the brightness output from the previous moment is maintained. The mutation threshold can be adaptively set according to the requirements for sensitivity to pseudo-mutations, preferably the P70 to P90 quantiles of the smoothed pseudo-mutation discrimination value distribution, to ensure effective differentiation of false changes caused by short-term occlusion, reflection, and vibration interference. The shielding command is implemented by writing to the MCU flag bit during actual execution, and the corresponding brightness output remains unchanged by the LED driver unit, thereby avoiding brightness jumps and flickering caused by pseudo-mutations. The original illumination pseudo-mutation discrimination value, the smoothed pseudo-mutation discrimination value, and the pseudo-mutation shielding flag are written into the brightness control database. The brightness control database records all historical discrimination results according to timestamps for subsequent brightness adjustment logic, parameter optimization, and on-site fault tracing.

[0042] In this implementation scheme, a stable and reliable pseudo-mutation filtering mechanism is constructed by boundary limiting, time smoothing, and threshold comparison of the illumination pseudo-mutation discrimination value. This mechanism can effectively distinguish between real illumination changes and short-term pseudo-mutations caused by occlusion, vibration, or noise, thus preventing the brightness adjustment process from being triggered erroneously. At the same time, by maintaining the brightness output of the previous moment through the shielding command, the probability of brightness jumps and flickering is significantly reduced, improving the smoothness and continuity of brightness adjustment. Combined with the complete record of the brightness control database, it has traceability and engineering maintainability, thereby ensuring that the overall brightness control process is more stable, robust, and adaptable to complex illumination environments.

[0043] Specifically, the process of obtaining the target brightness based on the trend illumination trajectory and mapping relationship, constructing an error suppression mechanism, and generating the brightness adjustment step size in conjunction with the mutation discrimination index is as follows: Obtain the current trend illumination value and duty cycle, and execute the brightness adjustment process: Obtain a fixed illumination brightness mapping function, specifically a linear mapping function, obtained by calibrating the ambient light response characteristics of the photosensitive device and the brightness output characteristics of the LED driver unit. This function is a factory parameter obtained by reading the device parameter table and is used to establish a one-to-one correspondence between the trend illumination value and the LED brightness output. Map the current trend illumination value to the target duty cycle and calculate the difference between the target duty cycle and the current duty cycle to obtain the duty cycle error. Based on a sliding time window, select the median of historical duty cycle errors as a typical adjustment scale value. Read the smoothed pseudo-mutation discrimination value and use the Sigmoid function to compress the pseudo-mutation discrimination value to obtain the pseudo-mutation compressed discrimination value. The function is used to limit the pseudo-mutation discrimination value to between 0 and 1, making the influence of pseudo-mutation intensity on the adjustment step size change smoothly and continuously, avoiding oscillations in the adjustment amount due to fluctuations in the pseudo-mutation discrimination value. The pseudo-mutation adjustment term is obtained by dividing the typical adjustment scale value by the sum of the constant and the pseudo-mutation compression discrimination value. The pseudo-mutation adjustment term decreases as the pseudo-mutation intensity increases, which can actively reduce the adjustment speed when a pseudo-mutation occurs, improving the stability of brightness adjustment. The normalized error suppression term is obtained by dividing the current duty cycle error by the typical adjustment scale value and performing a hyperbolic tangent operation. The hyperbolic tangent function can avoid adjustment overshoot caused by excessive duty cycle error, making the error update exhibit non-linear compression characteristics and improving the smoothness of adjustment. The brightness update step size value is obtained by multiplying the typical adjustment scale value and the normalized error suppression term, which is used to control the single change amplitude of the duty cycle, ensuring that the brightness update is responsive and does not introduce flicker or jumps, improving the continuity of the LED brightness adjustment process and the user's visual comfort.

[0044] The specific formula for the brightness update step size is as follows: ; In the formula, This represents the brightness update step size value. The brightness target is determined based on the trend illumination value. A normalized error suppression structure is constructed through typical adjustment scales. The adjustment step size is dynamically scaled using pseudo-mutation compression discriminant values. This ensures that the brightness follows the environmental changes in a timely manner while effectively suppressing brightness jumps caused by pseudo-mutations, thus achieving a smooth, steady-state, and flicker-free brightness adjustment process. This represents the typical adjustment scale value, used to characterize the typical adjustment amplitude scale of the current brightness change process, and serves as a reference quantity for the step size; This represents the pseudo-mutation compression discrimination value, used to measure whether there is a suspicion of pseudo-mutation in the current illumination, and is used as a denominator term to suppress the brightness step size; It indicates the target duty cycle, representing the target brightness that should be achieved based on the current lighting environment, and provides the direction and reference target for brightness adjustment; It represents the trend illumination value, used to eliminate noise interference such as instantaneous flicker, and provides illumination data that can truly reflect the trend of ambient light changes; This indicates the current duty cycle and the current brightness output status, used to compare with the target duty cycle to generate an error.

[0045] In this implementation scheme, a direct correspondence is established between the trend illumination value and the pre-calibrated illumination mapping function, ensuring physical consistency and device universality in the acquisition of the target duty cycle. Simultaneously, a typical adjustment scale value, a pseudo-abrupt compression discriminant value, and a hyperbolic tangent error suppression term are introduced during the brightness adjustment process to achieve adaptive control of the brightness update step size. This reduces the adjustment speed in pseudo-abrupt scenarios and maintains a sensitive response in real illumination change scenarios, effectively avoiding adjustment overshoot and brightness jumps. Consequently, the brightness adjustment process is smoother, more stable, and possesses anti-interference capabilities, significantly improving the continuity of LED brightness output and user visual comfort.

[0046] Specifically, the process of obtaining the updated duty cycle based on the brightness adjustment step size and executing boundary and rate-of-change limits is as follows: The brightness update step size value is added to the current duty cycle to obtain the updated duty cycle. The updated duty cycle is calculated in real-time by the main control MCU within the current sampling period and written to the LED driver unit via the PWM control interface, ensuring that the actual LED brightness is updated in the next refresh cycle, guaranteeing the real-time and executable nature of the adjustment action. The updated duty cycle is written to the LED driver unit as a new brightness adjustment command to adjust the actual brightness at the current moment. Boundary limits are executed on the updated duty cycle by comparing it with the highest and lowest duty cycle limit thresholds. The boundary limits are compared according to the highest and lowest duty cycle limit thresholds given in the device parameter table. If the updated duty cycle exceeds the allowable range, it is automatically truncated to the boundary value to prevent the LED from entering an unusable state of being too bright or too dim, avoiding hardware saturation and undetectable brightness. The updated duty cycle is limited to the range between the minimum and maximum duty cycle limits. A rate-of-change limit is applied to the updated duty cycle. If the absolute difference between the updated and current duty cycles is greater than the maximum duty cycle change threshold, the updated duty cycle is limited to the range between the current duty cycle minus the maximum duty cycle change threshold and the current duty cycle plus the maximum duty cycle change threshold. The maximum duty cycle change threshold is determined by visual stability requirements to avoid excessively large single adjustments that could cause flickering, jumps, and sudden brightness changes. The rate-of-change limit, by setting the maximum allowable step size for the duty cycle, makes the brightness change a linear, gradual process, improving the smoothness of the adjustment. The updated duty cycle is written to the brightness control database. The database records include a timestamp, updated duty cycle, adjustment step size, boundary, and a flag indicating whether the rate limit is effective. This data is used for subsequent algorithm backtracking, debugging, and adaptive optimization. A brightness adjustment completion flag is generated to notify the adjustment state machine that the brightness update has been successfully executed and can enter the power management decision process.

[0047] In this implementation scheme, by adding boundary and rate of change limits to the duty cycle update process, the brightness adjustment maintains real-time performance while avoiding flickering, abrupt changes, and visual discomfort caused by excessive changes. By writing the adjustment results into the database and generating a completion flag, the brightness control process is ensured to be traceable and manageable. The overall solution realizes a brightness update mechanism with controllable adjustment range, smooth brightness changes, and safe and stable hardware output, significantly improving the reliability, continuity, and user experience of LED brightness control.

[0048] Specifically, the process of determining the operating state based on vibration intensity and trend light trajectory changes, and implementing corresponding sampling frequency adjustments and power management strategies according to different operating states, is as follows: After brightness adjustment, the vibration count and trend light value change amplitude are read for state detection. The trend light value change amplitude is obtained by calculating the absolute difference between adjacent trend light values. The vibration count is used to characterize whether the device is in motion or operation; the trend light value change amplitude reflects whether the ambient light has changed significantly. The combination of the two is used to reliably determine the device's usage scenario. The vibration threshold and trend light change threshold serve as the basis for state switching and can be obtained from historical operating data statistics. If the vibration count is lower than the vibration threshold and the trend light value change amplitude is lower than the trend light change threshold, it is determined to be in a stationary state. The original photosensitive value sampling frequency is reduced, and the MCU enters low-power operation. In the stationary state, the sampling frequency is preferably reduced to 10% to 30% of the conventional sampling frequency to reduce unnecessary power consumption while maintaining basic monitoring capabilities for ambient light changes. The MCU's low-power operation mode is achieved by reducing the main frequency and shutting down non-critical peripherals to extend the device's battery life. If the vibration count is greater than or equal to the vibration threshold, or the change in trend illumination value is greater than or equal to the trend illumination change threshold, the system is determined to be in use, and the sampling frequency of the original photosensitive value is increased. In use, the sampling frequency is preferably restored to the rated sampling frequency to ensure sufficient response speed for the illumination link and brightness adjustment link. The state switching process is automatically executed by the main control module, and the switching action is completed within one sampling cycle to avoid cumulative delays affecting the real-time performance of brightness adjustment. All state determination results, sampling frequency adjustment records, and power consumption mode switching information are written to the brightness control database for subsequent algorithm optimization and operational analysis.

[0049] In this implementation scheme, by simultaneously monitoring vibration counts and trend illumination value changes, reliable determination of the device's usage scenario is achieved. The photosensitive sampling frequency and MCU power consumption mode are dynamically adjusted based on the stationary and usage states, significantly reducing unnecessary energy consumption while maintaining real-time brightness adjustment. Specifically, reducing the sampling frequency and entering low-power operation in the stationary state effectively extends battery life, while restoring the rated sampling frequency in the usage state ensures the sensitive response of the brightness adjustment chain. Through threshold statistics, adaptive switching, and full-process recording, this method improves the energy efficiency, stability, and environmental adaptability of brightness control.

[0050] Reference Figure 2As shown, the second aspect of this invention provides a low-power adaptive brightness control system based on photosensitivity, applied to the aforementioned low-power adaptive brightness control method based on photosensitivity. The system includes: a brightness data acquisition and processing module, used to acquire brightness control data in real time, perform amplitude screening on the brightness control data, construct local and extended windows, and extract ambient light features to obtain a basic brightness state set; a trend illumination smoothing calculation module, used to analyze the relative relationship between local and extended fluctuations based on the brightness state set, perform weighted fusion of illumination features to form a continuous and stable trend illumination trajectory, and combine historical time series to complete the inheritance and correction of trend illumination; and an illumination pseudo-abrupt change detection module, used to analyze the variation of local average illumination based on the brightness state set. The system analyzes the light rhythm and identifies the continuous change phase of illumination. It obtains a reference parameter set by combining historical data, integrates brightness control data, change rhythm, and illumination duration to construct a mutation discrimination index, distinguishes between real illumination changes and short-term pseudo-mutations, and outputs a mutation shielding flag. The anti-flicker brightness adjustment module obtains the target brightness based on the trend illumination trajectory and mapping relationship, constructs an error suppression mechanism, and generates a brightness adjustment step size based on the mutation discrimination index. It obtains the updated duty cycle based on the brightness adjustment step size and executes boundary constraints and change rate constraints. The low-power state management module determines the working state based on vibration intensity and trend illumination trajectory changes, and executes corresponding sampling frequency adjustment and power management strategies according to different working states.

[0051] This implementation scheme achieves a complete closed-loop control chain through modularization, encompassing ambient light acquisition, illumination feature extraction, trend illumination smoothing modeling, pseudo-mutation detection, flicker-proof brightness adjustment, and low-power state management. The modules maintain unified data and logical coherence, enabling stable differentiation between real illumination changes and pseudo-mutations under complex illumination disturbances and dynamic equipment usage scenarios. This achieves smooth, adaptive brightness adjustment and significantly reduces energy consumption through state-based sampling frequency switching. Overall, it offers advantages such as fast response, strong anti-interference capability, stable brightness changes, reduced flickering, and improved battery life, making it suitable for equipment and devices sensitive to ambient light changes and requiring long-term stable operation.

[0052] 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.

[0053] 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. As those skilled in the art will understand, 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. A low-power adaptive brightness control method based on photosensitivity, characterized in that, Includes the following steps: S1, real-time acquisition of brightness control data, amplitude screening of brightness control data, construction of local and extended windows and extraction of ambient light features to obtain basic brightness state set; S2, based on the analysis of the relative relationship between local and extended fluctuations of the brightness state set, performs weighted fusion of illumination features to form a continuous and stable trend illumination trajectory, and combines historical time series to complete the inheritance and correction of trend illumination. S3 analyzes the rhythm of local average illumination change based on the brightness state set, identifies the stage of continuous illumination change, obtains a reference parameter set by combining historical data, integrates brightness control data, rhythm of change and duration of illumination change to construct a mutation discrimination index, distinguishes between real illumination change and short-term pseudo-mutations, and outputs a mutation shielding flag. S4. The target brightness is obtained based on the trend illumination trajectory and mapping relationship, an error suppression mechanism is constructed, and a brightness adjustment step size is generated by combining the mutation discrimination index. The updated duty cycle is obtained by adjusting the step size based on the brightness, and boundary constraints and rate of change constraints are applied. S5 determines the working status based on the vibration intensity and trend light trajectory changes, and executes corresponding sampling frequency adjustment and power consumption management strategies according to different working statuses.

2. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process of acquiring real-time brightness control data, performing amplitude screening on the brightness control data, constructing local and expanded windows, extracting ambient light features, and obtaining the basic brightness state set is as follows: Real-time acquisition of brightness control data, including: original photosensitive value, vibration count, duty cycle of LED driver and timestamp; amplitude check based on the difference of the original photosensitive value at adjacent time points and the standard deviation of the original photosensitive value within the sliding window; if the difference exceeds the maximum illumination change amplitude threshold and the standard deviation is less than the fluctuation threshold, the sampling point is marked as an outlier and removed. A local sampling window and an extended sampling window are constructed based on fixed sampling numbers N and M, respectively. The average value and standard deviation of the original photosensitive values ​​within the local sampling window are calculated to obtain the local average illumination value and local fluctuation. Based on the extended sampling window, the average value and standard deviation of the original photosensitive values ​​are calculated to obtain the extended average illumination value and extended fluctuation. A brightness control database is constructed, and the brightness control data, local average illumination value, local fluctuation, extended average illumination value, and extended fluctuation are written into the brightness control database.

3. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process of analyzing the relative relationship between local and extended fluctuations based on the brightness state set, weighted fusion of illumination features to form a continuous and stable trend illumination trajectory, and combining historical time series to complete the inheritance and correction of trend illumination is as follows: Read the local volatility and extended volatility and sum them. Add the summation result to the minimum constant value to obtain the smoothed normalized baseline value. Divide the extended volatility by the smoothed normalized baseline value to obtain the trend illumination weighted value. The local average lighting weighted value is obtained by subtracting the trend lighting weighted value from the constant 1. Multiply the trend illumination weighted value by the trend illumination value of the previous time step to obtain the extended weighted inheritance term. If it is an initial calculation, the trend illumination value of the previous time step is assigned the extended average illumination value. Multiply the local average illumination weighted value by the current local average illumination value to obtain the local weighted update term. The trend illumination value at the current moment is obtained by adding the extended weighted inheritance term and the local weighted update term; The trend illumination value is calculated in real time, boundary checks and time synchronization are performed on the trend illumination value sequence, and the result is written to the brightness control database.

4. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process of analyzing the variation rhythm of local average illumination based on the brightness state set, identifying the stage of continuous illumination change, and obtaining the reference parameter set by combining historical data is as follows: Read the continuous sequence of local average illumination values, calculate the absolute rate of change of local average illumination values ​​at adjacent times using the difference method based on timestamps, and calculate the difference between the current local average illumination value and the local average illumination value at the previous time at each sampling time. If the difference exceeds the minimum effective change threshold, the illumination change is considered to be in a continuous stage. The timestamp differences of continuous stages are traced back from the current time and accumulated to form the duration. The accumulated duration is taken as the duration of the illumination change. Based on a sliding time window, the absolute change rate sequence of local average illumination value, the vibration count sequence, and the duration sequence of illumination change are read, and the median is selected as the benchmark value for the local average illumination value change rate, the vibration count sequence, and the duration sequence of illumination change, respectively.

5. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process for constructing abrupt change discrimination indicators by fusing brightness control data, change rhythm, and duration of illumination change is as follows: Divide the current duration of illumination change by the baseline value of illumination change duration, and take the opposite number as the exponent to perform natural exponential calculation to obtain the duration decay term; divide the current absolute rate of change of local average illumination value by the baseline value of local average illumination value change to obtain the normalized rate of change term; divide the current vibration count by the baseline value of vibration count to obtain the normalized vibration intensity term. Multiplying the duration decay term, the normalized rate of change term, and the normalized vibration intensity term yields the light pseudo-abrupt change discrimination value.

6. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process of distinguishing between real illumination changes and short-term pseudo-mutations, and outputting a mutation masking flag, is as follows: The calculated pseudo-mutation discrimination values ​​for illumination are restricted to the range of zero to the maximum boundary threshold; at the same time, the average value of the pseudo-mutation discrimination values ​​for illumination is calculated based on a sliding time window, and time smoothing is performed on the pseudo-mutation discrimination value sequence. The smoothed pseudo-mutation discrimination value is compared with the mutation threshold: if the pseudo-mutation discrimination value is less than the mutation threshold, the brightness adjustment process is initiated; if the pseudo-mutation discrimination value is greater than or equal to the mutation threshold, a pseudo-mutation shielding flag is generated, a shielding command is output, the brightness adjustment process is paused, and the brightness output of the previous moment is maintained. The original illumination pseudo-mutation discrimination value, the smoothed pseudo-mutation discrimination value, and the pseudo-mutation masking flag are written into the brightness control database.

7. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process of obtaining the target brightness based on the trend illumination trajectory and mapping relationship, constructing an error suppression mechanism, and generating the brightness adjustment step size in combination with the mutation discrimination index is as follows: Obtain the current trend illumination value and duty cycle, and execute the brightness adjustment process: obtain a fixed illumination brightness mapping function, map the current trend illumination value to the target duty cycle, and calculate the difference between the target duty cycle and the current duty cycle to obtain the duty cycle error; Based on the sliding time window, the median of the historical duty cycle error is selected as the typical adjustment scale value. Read the smoothed pseudo-mutation discriminant value, and use the Sigmoid function to compress the pseudo-mutation discriminant value to obtain the pseudo-mutation compressed discriminant value; The pseudo-mutation regulation term is obtained by dividing the typical regulation scale value by the sum of the constant and the pseudo-mutation compression discriminant value. Divide the current duty cycle error by the typical adjustment scale value and perform hyperbolic tangent operation to obtain the normalized error suppression term; multiply the typical adjustment scale value by the normalized error suppression term to obtain the brightness update step size value.

8. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process of obtaining the updated duty cycle based on the brightness adjustment step size and implementing boundary constraints and rate of change constraints is as follows: The brightness update step value is added to the current duty cycle to obtain the updated duty cycle. The updated duty cycle is then written as a new brightness adjustment command to the LED driver unit to adjust the actual luminous brightness at the current moment. Apply boundary constraints to the updated duty cycle by comparing the updated duty cycle with the highest duty cycle limit threshold and the lowest duty cycle limit threshold, and limiting it to the range between the lowest duty cycle limit threshold and the highest duty cycle limit threshold. Apply a rate-of-change limit to the updated duty cycle. If the absolute difference between the updated duty cycle and the current duty cycle is greater than the maximum duty cycle change threshold, then limit the updated duty cycle to the range between the current duty cycle minus the maximum duty cycle change threshold and the current duty cycle plus the maximum duty cycle change threshold. Write the updated duty cycle to the brightness control database and generate a brightness adjustment completion flag.

9. The low-power brightness adaptive control method based on photosensitivity as described in claim 1, characterized in that, The specific process of determining the working state based on changes in vibration intensity and trend light trajectory, and executing corresponding sampling frequency adjustment and power consumption management strategies according to different working states, is as follows: After brightness adjustment is completed, the vibration count and trend illumination value change amplitude are read for status detection. The trend illumination value change amplitude is obtained by calculating the absolute difference between adjacent trend illumination values. If the vibration count is lower than the vibration threshold and the trend illumination value change amplitude is lower than the trend illumination change threshold, it is determined to be in a static state, the photosensitive raw value sampling frequency is reduced, and the MCU enters low-power operation. If the vibration count is greater than or equal to the vibration threshold or the trend illumination value change amplitude is greater than or equal to the trend illumination change threshold, it is determined to be in a working state, and the photosensitive raw value sampling frequency is increased.

10. A low-power brightness adaptive control system based on photosensitivity, characterized in that, include: The brightness data acquisition and processing module is used to acquire brightness control data in real time, perform amplitude screening on the brightness control data, construct local windows and extended windows, extract ambient light features, and obtain a basic brightness state set. The trend lighting smoothing calculation module is used to analyze the relative relationship between local and extended fluctuations based on the brightness state set, perform weighted fusion of lighting features to form a continuous and stable trend lighting trajectory, and combine historical time series to complete the inheritance and correction of trend lighting. The illumination pseudo-mutation detection module is used to analyze the change rhythm of local average illumination based on the brightness state set, identify the stage of continuous illumination change, obtain a reference parameter set by combining historical data, integrate brightness control data, change rhythm and illumination duration to construct a mutation discrimination index, distinguish between real illumination change and short-term pseudo-mutations, and output a mutation shielding flag. The anti-flicker brightness adjustment module is used to obtain the target brightness based on the trend illumination trajectory and mapping relationship, construct an error suppression mechanism, and generate a brightness adjustment step size in combination with abrupt change discrimination index; The updated duty cycle is obtained by adjusting the step size based on the brightness, and boundary constraints and rate of change constraints are applied. The low-power state management module is used to determine the working state based on the vibration intensity and trend light trajectory changes, and to execute corresponding sampling frequency adjustment and power management strategies according to different working states.

Images