A color temperature adjustable lighting device and a control method thereof

Abstract

The application discloses a color temperature adjustable lighting device and a control method thereof, and belongs to the technical field of intelligent lighting. The application aims to solve the problems of the existing color temperature adjustable lighting device, such as difficulty in adapting to complex environmental light changes, insufficient fusion of user personalized needs, and poor color temperature stability. The sensing interaction module collects environmental light and user control instructions, the execution decision module constructs an evaluation dataset based on the environmental light collection time, quantifies the complexity of the environmental light through multi-dimensional feature extraction and performs a deep evaluation, and then, after obtaining the initial target color temperature combined with the user control instruction, a graded control strategy is started according to the complexity level to generate a color temperature gradual adjustment curve. The intelligent control module dynamically calculates and corrects the deviation of the driving parameters according to the color temperature gradual adjustment curve, so as to realize accurate color temperature regulation and control. Through the environmental light complexity evaluation and the graded control strategy, the application improves the adaptive matching ability and long-term stability of the lighting color temperature and the environment and user needs.

Description

Technical Field

[0001] This invention relates to the field of intelligent lighting technology, and more specifically to an adjustable color temperature lighting device and its control method. Background Technology

[0002] In the field of smart lighting, adjustable color temperature lighting devices have been widely used in homes, offices, and public spaces. However, existing technologies for traditional adjustable color temperature lighting devices largely rely on fixed parameter settings or simple ambient light sensing, making it difficult to cope with complex and ever-changing lighting environments. On the one hand, some devices can only adjust based on single illuminance or color temperature data. When there are sudden changes or high-frequency fluctuations in ambient light color temperature, such as the periodic changes in sunlight passing through blinds or frequent switching of external lights, they cannot accurately identify the complexity of the ambient light, leading to delayed or over-responding color temperature adjustments and affecting visual comfort. On the other hand, existing devices have limited ability to integrate personalized user needs. For example, after a user sets a specific scene mode, it is difficult to dynamically optimize the color temperature parameters in conjunction with real-time ambient light, easily resulting in mismatches with the ambient light. Furthermore, during long-term operation, affected by LED light decay, junction temperature changes, and grid voltage fluctuations, traditional devices lack effective color temperature drift suppression mechanisms, resulting in poor output color temperature stability and a shortened lifespan. These problems make it difficult for existing adjustable color temperature lighting devices to simultaneously meet environmental adaptability, user-specific needs, and long-term stability in practical applications. Therefore, in order to overcome these limitations, this invention proposes an adjustable color temperature lighting device and its control method. Summary of the Invention

[0003] To address the shortcomings of existing technologies, the present invention aims to provide an adjustable color temperature lighting device and its control method. It is dedicated to solving the problem that existing lighting devices are unable to accurately, dynamically, and stably adjust the color temperature according to complex and ever-changing ambient light conditions such as sudden color temperature changes, illuminance fluctuations, and personalized user needs. Through technologies such as ambient light complexity assessment, hierarchical control strategies, and dynamic correction of driving parameters, the invention achieves adaptive matching of lighting color temperature with the environment and user needs, thereby improving lighting comfort and stability.

[0004] To achieve the above objectives, the present invention provides the following technical solution:

[0005] A method for controlling an adjustable color temperature lighting device, comprising:

[0006] Collect ambient light data and receive user control commands. Ambient light data includes color temperature data and illuminance data. User control commands include forced color temperature value and scene mode.

[0007] Based on the ambient light data acquisition time, an evaluation dataset is constructed and a preliminary ambient light complexity assessment is performed. By dividing the data into sliding windows, the slope of color temperature change and the illuminance fluctuation coefficient are extracted, abnormal sliding windows are identified, and the ambient light complexity is quantified based on the proportion of abnormal sliding windows to determine the current ambient light complexity.

[0008] If the current ambient light complexity is determined to be low, then the depth complexity is evaluated by combining the time matching degree of the evaluation dataset to determine whether the current ambient light complexity should be adjusted to high complexity.

[0009] The initial target color temperature is obtained based on user control commands, and a hierarchical control strategy is initiated for the initial target color temperature according to the complexity of ambient light:

[0010] If the ambient light complexity is low, a simple target matching strategy is adopted, which dynamically compensates the initial target color temperature based on the illuminance deviation ratio to generate a color temperature gradient adjustment curve. If the ambient light complexity is high, a dynamic target matching strategy is adopted, which performs abrupt analysis on the ambient light complexity to determine whether the ambient light change is periodic, and dynamically adjusts the initial target color temperature to generate a color temperature gradient adjustment curve.

[0011] Based on the color temperature gradient adjustment curve, the driving parameters of the lighting device are dynamically calculated and deviations are corrected to control the color temperature.

[0012] Specifically, the steps of the dynamic target matching strategy include:

[0013] Perform periodic analysis on the color temperature and illuminance value sequences to determine whether the changes in ambient light are periodic and mark the delay period;

[0014] If it is determined to be periodic, the adjustment factor parameter is configured, the look-ahead compensation mechanism is activated according to the delay period, and the advance adjustment time of the look-ahead compensation is calculated based on the delay period and the adjustment factor parameter.

[0015] Configure a historical delay sliding window, fit a linear trend line to the color temperature change sequence within the historical delay sliding window, calculate the color temperature change rate per unit time, and determine the compensation direction for the current delay period.

[0016] Configure the fluctuation analysis window, calculate the average color temperature value within the fluctuation analysis window, and use the average color temperature fluctuation within the delay period as the compensation amount to correct the initial target color temperature based on the determined compensation direction, thus serving as the dynamic target color temperature.

[0017] Calculate the color temperature difference between the actual output color temperature of the current lighting device and the dynamic target color temperature, decompose the advance adjustment time into equally spaced time points to form an adjustment time axis, perform smooth transition processing on the current color temperature value and the dynamic target color temperature, generate a color temperature adjustment sequence and perform smoothing processing, and generate a color temperature gradient adjustment curve.

[0018] If the color temperature is determined to be non-periodic, a mutation buffer mechanism is triggered to set a cooling-off period. During the cooling-off period, the color temperature value sequence is processed by moving average to extract the color temperature trend value. If the color temperature trend value deviates from the initial target color temperature by more than a preset smoothing threshold, a slow adjustment strategy is initiated. A dynamic target color temperature is generated through weighted fusion, and then a color temperature gradient adjustment curve is generated through linear interpolation and filtering.

[0019] Specifically, the steps for conducting a preliminary assessment of ambient light complexity include:

[0020] A color temperature value sequence and an illuminance value sequence are constructed based on the acquisition time of ambient light data, and the color temperature value sequence and the illuminance value sequence are aligned in the time dimension using a timestamp calibration algorithm;

[0021] The color temperature and illuminance sequences are filtered, cleaned, outlier identified, and repaired using linear interpolation.

[0022] Configure the evaluation time window, filter the data of color temperature value sequence and illuminance value sequence within the evaluation time window before the current timestamp, and construct the evaluation dataset;

[0023] Set the sliding window and sliding step size, calculate the color temperature difference between adjacent data points in the color temperature value sequence within the sliding window and the slope per unit time, and obtain the color temperature change slope of the sliding window based on the slope per unit time within the sliding window.

[0024] Calculate the mean and standard deviation of the illuminance value sequence within the sliding window, and obtain the illuminance fluctuation coefficient by calculating the fluctuation coefficient within the sliding window;

[0025] Configure the color temperature change threshold and illuminance fluctuation threshold, and mark the color temperature change window and illuminance fluctuation window according to the color temperature change slope and illuminance fluctuation coefficient of the sliding window;

[0026] Configure complexity thresholds and statistically evaluate the proportion of color temperature abrupt change windows and illuminance fluctuation windows within the dataset to determine the current ambient light complexity, including low complexity and high complexity.

[0027] Specifically, the steps for evaluating depth complexity by combining the time matching degree of the evaluation dataset include:

[0028] If the current ambient light complexity is determined to be low, configure the historical data collection cycle and extract historical ambient light datasets with the same date type and the same timestamp;

[0029] Statistical analysis was performed on historical ambient light datasets to calculate the mean and standard deviation of color temperature and illuminance, and to obtain the fluctuation range of color temperature and illuminance.

[0030] The average color temperature and average illuminance of the evaluation dataset are compared with the fluctuation range. If they are within the fluctuation range, the time matching degree is judged to be up to standard; otherwise, the time matching degree is judged to be abnormal.

[0031] If the time matching is determined to be abnormal, the current ambient light complexity is adjusted from low complexity to high complexity.

[0032] Specifically, the steps of a simple target matching strategy include:

[0033] Configure a smoothing period, extract the average color temperature and average illuminance within the smoothing period of the current timestamp, if the deviation between the current illuminance value and the average illuminance value is greater than the illuminance deviation threshold, then determine the ambient light deviation and calculate the product of the illuminance deviation ratio and the average color temperature as the environmental compensation parameter, and generate a dynamic target color temperature by weighted fusion of the initial target color temperature and the environmental compensation parameter.

[0034] Configure a grading threshold and calculate the color temperature difference between the actual output color temperature of the current lighting device and the dynamic target color temperature. If the color temperature difference is greater than the grading threshold, a phased adjustment mode is adopted, and the adjustment stages are divided according to the proportion of the color temperature difference. Each stage sets the single adjustment amplitude according to the preset adjustment gradient to generate a color temperature adjustment sequence; otherwise, the preset adjustment gradient is used to generate a color temperature adjustment sequence.

[0035] The smoothing cycle duration is decomposed into equally spaced time points. Based on the total amplitude of the color temperature adjustment sequence and the time axis, the color temperature adjustment sequence is optimized and smoothed to generate a color temperature gradient adjustment curve.

[0036] Specifically, the steps for obtaining the initial target color temperature based on user control commands include:

[0037] Parse the user control command and determine if there is a forced color temperature setting. If there is, use the forced color temperature value as the initial target color temperature.

[0038] Configure warning thresholds, retrieve the standard color temperature value for the corresponding time period from the time scene color temperature benchmark library based on the current timestamp, calculate the absolute value of the deviation between the standard color temperature value and the forced color temperature value, and trigger a multi-dimensional color temperature warning if the absolute value of the deviation is greater than the warning threshold.

[0039] If no forced color temperature setting exists, determine whether a scene mode is set. If a scene mode is set, retrieve the scene color temperature corresponding to the current scene mode from the local policy library as the initial target color temperature.

[0040] If no scene mode is set, the standard color temperature value for the corresponding time period will be retrieved from the time scene color temperature reference library based on the current timestamp as the initial target color temperature.

[0041] Specifically, the steps to determine whether changes in ambient light are periodic include:

[0042] Configure an autocorrelation window to extract subsequences of color temperature and illuminance values ​​within the autocorrelation window prior to the current timestamp;

[0043] Given a time delay sequence, for the color temperature value subsequence and the illuminance value subsequence, at each time delay point of the time delay sequence, calculate the autocorrelation coefficient using the autocorrelation function to generate an autocorrelation coefficient sequence;

[0044] Configure a periodic threshold, traverse the autocorrelation coefficient sequence, and determine whether the ambient light change is periodic by judging whether the autocorrelation coefficient corresponding to the time delay is greater than the periodic threshold. If it is periodic, select the time delay as the delay period.

[0045] Specifically, the steps for dynamically calculating and correcting the driving parameters of the lighting device and adjusting the color temperature include:

[0046] The output brightness of the lighting device is divided into intervals, and the interval to which the current output brightness of the lighting device belongs is determined, including low brightness interval, medium brightness interval and high brightness interval, and the nonlinear light mixing model of the corresponding interval is called.

[0047] The nonlinear color mixing model is used to establish a nonlinear mapping relationship between the input vector and the PWM duty cycle of the warm and cool light channels. The elements of the input vector include: target color temperature value, brightness range, LED junction temperature value, and drive current deviation.

[0048] LED junction temperature data is collected, driving current feedback signals of the warm and cool light channels are obtained, and input vectors are constructed based on the target color temperature value of the color temperature gradient adjustment curve, combined with preset spectral ratio parameters.

[0049] A nonlinear light mixing model is used to perform forward propagation calculations to generate the basic PWM duty cycle for the warm and cold light channels.

[0050] Specifically, the steps for dynamically calculating and correcting the driving parameters of the lighting device and adjusting the color temperature also include:

[0051] If the output brightness of the current lighting device belongs to the low brightness range, a correction coefficient for the duty cycle of the cold light channel is generated to compensate for the color temperature shift and adjust the basic PWM duty cycle.

[0052] Based on the collected LED junction temperature data, the pre-calibrated temperature and color temperature drift curve is called to generate a dynamic temperature compensation coefficient. The dynamic temperature compensation coefficient is weighted by the PID controller to adjust the basic PWM duty cycle.

[0053] The actual driving current of the warm and cold light channels is obtained and compared with the preset current value to calculate the current deviation. When the current deviation exceeds the preset current deviation threshold, the adaptive control algorithm is activated to correct the current deviation in real time by adjusting the duty cycle of the basic PWM.

[0054] An aging model is pre-established based on the LED light decay characteristics. Combined with the cumulative operating time of the lighting device and historical light decay data, the current light decay level is calculated, the corresponding luminous flux compensation coefficient is obtained, the update cycle is configured, and the luminous flux compensation coefficient is updated periodically to adjust the basic PWM duty cycle.

[0055] The actual output color temperature of the lighting device is monitored in real time and compared with the target color temperature value. The color temperature deviation between the actual output color temperature and the target color temperature value is calculated. If it is greater than the preset color temperature deviation threshold, a secondary correction mechanism is triggered to correct the PWM duty cycle.

[0056] An adjustable color temperature lighting device includes a perception and interaction module, an execution and decision-making module, and an intelligent control module:

[0057] The perception and interaction module is used to collect ambient light data and receive user control commands; the execution and decision-making module is used to classify and judge the complexity of ambient light based on ambient light data, determine the initial target color temperature in combination with user control commands, and generate a color temperature gradient adjustment curve; the intelligent control module is used to dynamically calculate and correct the deviation of the driving parameters of the lighting device according to the color temperature gradient adjustment curve, and to control the color temperature.

[0058] The beneficial effects of this invention are:

[0059] This invention collects ambient light data and user control commands, employs sliding window algorithms and time-matching degree evaluation techniques to quantify ambient light complexity, and achieves precise response to lighting environments of varying complexity based on a hierarchical control strategy. It utilizes a nonlinear mixing model combined with LED junction temperature, current feedback, and other multi-parameter dynamic correction of driving parameters to compensate for low-brightness shifts and long-term light decay. Simultaneously, it deeply integrates user-specific needs and scene modes. Overall, this solution effectively solves the problems of poor environmental adaptability, lagging color temperature adjustment, insufficient stability, and weak personalization integration in traditional lighting devices. It achieves intelligent matching of lighting color temperature with environmental changes and user needs, significantly improving the accuracy, response speed, and long-term stability of color temperature adjustment, providing users with a more comfortable and intelligent lighting experience. Attached Figure Description

[0060] Figure 1This is a flowchart of a control method for an adjustable color temperature lighting device according to the present invention;

[0061] Figure 2 This is a flowchart illustrating the specific steps involved in determining the ambient light complexity of this invention.

[0062] Figure 3 This is a flowchart for determining the ambient light complexity at a depth according to the present invention;

[0063] Figure 4 A flowchart of the specific steps for obtaining the initial target color temperature of the lighting device according to the present invention;

[0064] Figure 5 This is a flowchart illustrating the specific steps of the dynamic target matching strategy of the present invention;

[0065] Figure 6 This is a schematic diagram of the structure of an adjustable color temperature lighting device according to the present invention. Detailed Implementation

[0066] Example 1:

[0067] Please see Figure 1 This embodiment describes a control method for an adjustable color temperature lighting device, including:

[0068] Step S1: Collect ambient light data, including color temperature and illuminance data, through sensors, and receive user control commands, including forced color temperature and scene mode; so that the lighting device can adaptively trigger the switching of control mode based on the stability characteristics of ambient light, and simultaneously receive personalized user needs.

[0069] In this embodiment, ambient light data acquisition and command reception are achieved by integrating a spectral color temperature sensor and a multimodal interaction component. The ambient light data sensing part adopts a multi-level optical signal acquisition architecture: the bottom layer deploys composite photosensitive elements covering the entire visible light spectrum; the middle layer is configured with adaptive filtering circuits to suppress ambient light interference; and the top layer runs a light feature extraction algorithm to analyze the frequency of color temperature changes and illuminance fluctuation trends in real time. The user interaction part integrates a capacitive touch panel and far-field voice recognition. The touch interface supports gesture-based adjustment of the color temperature gradient and selection of scene modes, such as meeting mode, reading mode, and sleep mode. It interconnects with the smart home system through a communication module, synchronizing user-preset parameters to the edge control unit, forming a two-way data input channel for ambient light characteristics and user preferences. By dynamically adjusting the optical signal sampling frequency and the user control command priority management mechanism, it ensures accurate extraction of ambient light stability characteristics and the establishment of personalized light demand models even in scenarios with strong light interference or multi-user control.

[0070] Step S2: Based on the collected ambient light data, extract illumination features from the time and frequency domains to quantify ambient light complexity and make a preliminary ambient light complexity judgment. If the ambient light is determined to be of low complexity, then combine the time matching degree and human body state to make a deep ambient light complexity judgment. A graded control strategy is activated according to the environmental complexity level to obtain the color temperature gradient adjustment curve of the lighting device.

[0071] In this embodiment, feature fusion is performed on ambient light data, and a sliding window algorithm is used to calculate the slope of color temperature abrupt changes and the illuminance fluctuation coefficient. Short-time Fourier transform is combined to identify high-frequency interference features to complete the initial complexity assessment. For scenes determined to be low-complexity, a time model constructed from historical light data of the same time period is further called to calculate the real-time deviation rate for further complexity assessment. A graded control strategy is initiated for different levels. The initial target color temperature of the lighting device is obtained: for low-complexity scenes, the local storage time and time scene color temperature benchmark library are directly mapped, and a human body state fine-tuning coefficient is superimposed; for high-complexity scenes, cloud collaboration is triggered, and a dynamic color temperature curve is generated based on the regional light environment database and real-time meteorological data, combined with a spectral fitting algorithm to optimize the warm and cool light ratio.

[0072] Please see Figure 2 Preferably, the specific steps for determining the complexity of ambient light include:

[0073] Based on the acquisition time of ambient light data, an ambient light data sequence is constructed, including a color temperature value sequence and an illuminance value sequence. The color temperature value sequence and the illuminance value sequence are aligned in time dimension by a timestamp calibration algorithm to eliminate the feature calculation deviation caused by asynchronous sampling, thus laying the foundation for subsequent spatiotemporal consistency analysis.

[0074] The color temperature and illuminance value sequences are cleaned by filtering to remove random noise and identify outliers. Linear interpolation is then used to repair the outliers, ensuring the continuity and authenticity of the input data and providing a reliable data foundation for subsequent analysis.

[0075] Configure the evaluation time window, filter the color temperature value sequence and illuminance value sequence within the evaluation time window before the current timestamp, construct the evaluation dataset, and conduct a preliminary assessment of the ambient light complexity; by limiting the analysis scope, focus on the characteristics of recent light environment changes, and avoid the interference of historical data on real-time judgment.

[0076] Based on the dynamic nature of the scene, the sliding window and sliding step size are set. For the color temperature value sequence within the sliding window, the color temperature difference between adjacent data points is calculated and divided by the time interval between adjacent data points to obtain the slope per unit time. The maximum absolute value of the slope within the sliding window is selected as the slope of the color temperature change in that sliding window, highlighting the impact of significant change events.

[0077] The mean and standard deviation of the illuminance value sequence within the sliding window are calculated, and the fluctuation coefficient within the sliding window is used as the illuminance fluctuation coefficient of the sliding window to eliminate the absolute value difference under different illuminance levels.

[0078] Configure color temperature abrupt change threshold and illuminance fluctuation threshold to identify abnormal sliding windows with significant changes in the light environment. If the slope of the color temperature abrupt change of the sliding window is greater than the color temperature abrupt change threshold, the sliding window is marked as a color temperature abrupt change window, indicating that there is a significant dynamic change in color. If the illuminance fluctuation coefficient of the sliding window is greater than the illuminance fluctuation threshold, the sliding window is marked as an illuminance fluctuation window, indicating that there is a significant dynamic change in brightness.

[0079] Configure a complexity threshold to quantify the significance of dynamic changes in the light environment within the evaluation dataset. Statistically evaluate the proportion of color temperature abrupt change windows and illuminance fluctuation windows within the evaluation dataset. If the proportion of color temperature abrupt change windows within the evaluation dataset is less than the complexity threshold, and the proportion of illuminance fluctuation windows within the evaluation dataset is less than the complexity threshold, then the current ambient light complexity is determined to be low complexity; otherwise, the current ambient light complexity is determined to be high complexity.

[0080] Please see Figure 3 If the current ambient light complexity is determined to be low, then a deep ambient light complexity assessment is performed by combining the time matching degree of the evaluation dataset. The complexity rating is further calibrated from two dimensions: temporal regularity and human activity characteristics, thus achieving a deep determination of ambient light complexity.

[0081] Configure historical data collection cycles to systematically collect and store historical ambient light data. Extract and evaluate historical ambient light datasets with the same date type and timestamp as the benchmark data for determining whether the current light environment conforms to temporal patterns. Date types include weekdays and weekends.

[0082] Statistical analysis was performed on historical ambient light datasets to calculate the mean and standard deviation of color temperature and illuminance, and to obtain the fluctuation range of color temperature and illuminance.

[0083] The average color temperature and illuminance of the evaluation dataset are calculated and compared with the fluctuation range of color temperature and illuminance in the historical ambient light dataset. If they are within the fluctuation range, the time matching degree is considered satisfactory, indicating that the change in the light environment conforms to the historical pattern of that period; otherwise, the time matching degree is considered abnormal, suggesting possible unexpected changes in the light environment. If the time matching degree is determined to be abnormal, the current ambient light complexity is adjusted from low complexity to high complexity. This effectively identifies regular scenarios such as seasonal light changes and timed device linkages, reducing the false judgment rate of low complexity scenarios and ensuring timely response to sudden abnormal light environments.

[0084] Please see Figure 4Preferably, the specific steps for obtaining the initial target color temperature of the lighting device include:

[0085] The system analyzes received user control commands to determine if a forced color temperature setting exists. If the user sets a forced color temperature value via touch panel, voice command, or smart home system, this forced color temperature value is used as the initial target color temperature, and a warning threshold is configured simultaneously. The warning threshold is set based on the reasonable fluctuation range of the standard color temperature value for the corresponding time period in the time-scene color temperature benchmark library. The system respects the user's personalized needs, using the user-defined color temperature as the primary reference.

[0086] Based on the current timestamp, the standard color temperature value for the corresponding time period is retrieved from the time-scene color temperature benchmark library, and the absolute value of the deviation between the standard color temperature value and the forced color temperature value is calculated. If the absolute value of the deviation is greater than the warning threshold, a color temperature warning is triggered, and a color temperature anomaly reminder is pushed through the mobile APP, a flashing indicator light is displayed on the local touch panel, and the graphic and text information "Current color temperature deviates from the recommended range" is displayed on the human-machine interface of the lighting device to remind the user that the current color temperature may affect visual comfort or human circadian rhythm.

[0087] If the user has not set a mandatory color temperature, the system further determines whether the user has set a scene mode. Scene mode preset parameters are stored in a local policy library, including scene color temperature, illuminance, and spectral ratio for specific scenes. When a scene mode command is detected, the scene color temperature of the current scene mode is retrieved as the initial target color temperature. This quickly matches suitable color temperature parameters to meet the lighting needs of different scenes and improve the user experience.

[0088] If no scene mode is set, the standard color temperature value for the corresponding time period is retrieved from the time scene color temperature reference library based on the current timestamp as the initial target color temperature. The time scene color temperature reference library integrates standard color temperature values ​​for regular color temperature references for different date types and different time periods, providing default and reasonable color temperature settings for scenes without explicit user control instructions, ensuring basic lighting adaptability.

[0089] If the ambient light complexity is determined to be low, a simple target matching strategy is used to adjust the initial target color temperature to obtain a color temperature gradient adjustment curve. The specific steps of the simple target matching strategy include:

[0090] A smoothing period is configured to stabilize ambient light data within the current time period. The average color temperature and average illuminance are extracted within the smoothing period of the current timestamp. If the deviation between the current illuminance value and the average illuminance value exceeds a preset illuminance deviation threshold, ambient light deviation is determined. The product of the illuminance deviation ratio and the average color temperature is calculated as an environmental compensation parameter. A dynamic target color temperature adapted to the current illuminance characteristics is generated by weighted fusion of the initial target color temperature and the environmental compensation parameter. Based on stable changes in ambient light, the initial target color temperature is fine-tuned to match the lighting color temperature with the ambient brightness, avoiding visual discomfort caused by changes in ambient light. If the deviation between the current illuminance value and the average illuminance value is less than or equal to the preset illuminance deviation threshold, ambient light deviation is determined, and the initial target color temperature is used as the dynamic target color temperature.

[0091] The system configures a tiered threshold and calculates the color temperature difference between the actual output color temperature of the current lighting device and the dynamic target color temperature. Based on the magnitude of this color temperature difference, a tiered adjustment strategy is initiated. If the color temperature difference exceeds the tiered threshold, a phased adjustment mode is adopted. The adjustment is divided into several phases according to the proportion of the color temperature difference. For each phase, a single adjustment amplitude is set according to a preset adjustment gradient, generating a color temperature adjustment subsequence for that phase, ultimately obtaining the final color temperature adjustment sequence. Otherwise, a preset adjustment gradient is used to set the single adjustment amplitude, generating the color temperature adjustment sequence. Through tiered adjustment, the system can avoid the visual impact of sudden color temperature changes when the temperature difference is large, and can quickly complete fine-tuning when the temperature difference is small, achieving precise control.

[0092] The smoothing cycle duration is decomposed into equally spaced time points. Based on the total amplitude and time axis of the color temperature adjustment sequence, the sequence is optimized using linear interpolation or S-curve interpolation algorithms. A first-order low-pass filter algorithm is then used to smooth the color temperature adjustment sequence, generating a color temperature gradient adjustment curve. This ensures a smooth and natural color temperature adjustment process that conforms to the human eye's adaptation to changes in light, thereby improving visual comfort.

[0093] Please see Figure 5 If the ambient light complexity is determined to be high, a dynamic target matching strategy is used to eliminate unstable and changing factors. By performing abrupt change analysis on the ambient light complexity, it is determined whether the ambient light changes are periodic. Based on the initial target color temperature, dynamic adjustment is performed to obtain a color temperature gradient adjustment curve. The specific steps of the dynamic target matching strategy include:

[0094] Periodic analysis is performed on the color temperature and illuminance value sequences. Based on the scene's basic dynamics, an autocorrelation window is configured, and subsequences of color temperature and illuminance values ​​within the autocorrelation window before the current timestamp are extracted to ensure that sufficient light signal fluctuation characteristics are included.

[0095] Autocorrelation analysis was performed on the color temperature and illuminance subsequences. A scanning range for the time delay sequence was defined, such as continuous time intervals from short to long delays, to traverse and analyze the correlation between the color temperature and illuminance subsequences at different time delays. The minimum interval and maximum range of the time delay sequence were dynamically adjusted based on the autocorrelation window duration to ensure coverage of possible periodic intervals.

[0096] For the color temperature value subsequence and the illuminance value subsequence, at each time delay point of the time delay sequence, the autocorrelation coefficient between the signal and its delayed version is calculated through the autocorrelation function to generate the corresponding autocorrelation coefficient sequence, thereby quantifying the similarity between the color temperature value subsequence and the illuminance value subsequence under different delays.

[0097] Configure a periodic threshold, traverse the autocorrelation coefficient sequence, and determine if there exists a color temperature value subsequence or illuminance value subsequence with an autocorrelation coefficient greater than the periodic threshold under a certain time delay. If so, the ambient light change under that time delay is determined to be periodic. The time delays that meet the condition are marked as the delay period, serving as a periodic characteristic parameter of the ambient light change. If multiple time delays meet the condition, the time delay with the largest autocorrelation coefficient is selected as the delay period. Otherwise, the ambient light change is determined to be non-periodic.

[0098] If the ambient light change is determined to be periodic, the adjustment factor parameter is configured based on the scene dynamics and the magnitude of the light environment change. For example, a larger factor is set for rapidly changing scenes and a smaller factor is set for static scenes. When the ambient light change is determined to be periodic, the look-ahead compensation mechanism is activated according to the delay period. The advance adjustment time of the look-ahead compensation is calculated based on the confirmed delay period and the adjustment factor parameter. For example, the delay period is multiplied by the adjustment factor parameter as the advance adjustment time to ensure that the regulation is activated before the periodic change of the light environment occurs, so as to achieve predictive regulation.

[0099] Configure a historical delay sliding window, setting its length to an integer multiple of the delay period to ensure sufficient historical color temperature variation data is included. Align the color temperature sequences for each period using a timestamp calibration algorithm to eliminate analysis bias caused by time offsets.

[0100] For the color temperature change sequence within the historical delay sliding window, a linear trend line is fitted using the least squares method to calculate the color temperature change rate per unit time. If the slope is positive, it indicates that the color temperature is increasing within the historical period; if the slope is negative, it indicates a decreasing trend. This determines the compensation direction for the current delay period.

[0101] Configure a fluctuation analysis window to cover color temperature data for a single delay period, calculate the average color temperature value within the fluctuation analysis window, and use the average color temperature fluctuation within the delay period as the compensation amount to correct the initial target color temperature based on the determined compensation direction, thus serving as the dynamic target color temperature.

[0102] The color temperature difference between the actual output color temperature of the current lighting device and the dynamic target color temperature is calculated. The advance adjustment time is decomposed into equally spaced time points according to a preset granularity to form an adjustment time axis. Cubic spline interpolation or S-curve interpolation algorithms are used to smooth the transition between the current color temperature and the dynamic target color temperature to generate a color temperature adjustment sequence. A first-order low-pass filtering algorithm is used to smooth the color temperature adjustment sequence to generate a color temperature gradient adjustment curve.

[0103] If the ambient light change is determined to be non-periodic, a sudden change buffer mechanism is triggered, a cool-down period is set, and the current color temperature value is locked. The cool-down period is used to prevent the color temperature of the lighting device from oscillating frequently due to short-term fluctuations in ambient light when the ambient light change is non-periodic.

[0104] The color temperature value sequence during the cooling-off period is processed by moving average to extract the color temperature trend value. If the color temperature trend value deviates from the current initial target color temperature by more than a preset smoothing threshold, a slow adjustment strategy is initiated. The preset smoothing threshold is used to measure whether the change in ambient light has reached a significant level that requires color temperature adjustment.

[0105] In the slow adjustment strategy, a dynamic target color temperature is generated based on the initial target color temperature and color temperature trend value through weighted fusion. The cooling-off period is decomposed into equally spaced time points. Based on the difference between the dynamic target color temperature and the current actual color temperature, a color temperature adjustment sequence is generated through linear interpolation. A first-order low-pass filtering algorithm is used to smooth the color temperature adjustment sequence, generating a color temperature gradient adjustment curve.

[0106] Step S3: Based on the color temperature gradient adjustment curve, the driving parameters are dynamically calculated and the deviation is corrected. By running a nonlinear light mixing model trained based on historical data and integrating real-time collected LED junction temperature data and driving current feedback signals, the PWM duty cycle of the warm and cold light channels is optimized in real time to compensate for color temperature shift under low brightness. The driving parameters are also corrected online through temperature compensation coefficient and power grid fluctuation feedforward algorithm to ensure that the target color temperature remains stable during long-term operation of the lamp or voltage fluctuations, and to suppress color temperature drift caused by light decay and junction temperature changes.

[0107] In this embodiment, relying on a nonlinear mixing model trained based on historical data, the real-time collected LED junction temperature data and drive current feedback signal are deeply integrated. By dynamically calculating the PWM duty cycle of the warm and cool light channels, the target color temperature is accurately tracked. In particular, for the color temperature shift problem in low-brightness scenarios, a targeted correction is made through the model's built-in compensation mechanism. At the same time, by using the temperature compensation coefficient to respond in real time to the impact of LED junction temperature changes on color temperature, the drive parameters are continuously corrected online during the long-term operation of the lamp, effectively suppressing color temperature drift caused by factors such as light decay and junction temperature fluctuations, and ensuring the stability and consistency of the output color temperature under different usage scenarios.

[0108] Preferably, the specific steps for dynamically calculating and correcting the deviation of the driving parameters include:

[0109] The output brightness of the lighting device is divided into ranges based on the nonlinear optical characteristics and color temperature shift of LEDs under different currents, resulting in low, medium, and high brightness ranges. This approach combines the nonlinear perception of brightness by the human eye with the electrical and optical conversion characteristics of LED devices to ensure the specificity of the light mixing model within each range. The system determines the current output brightness range of the lighting device and then calls the corresponding nonlinear light mixing model.

[0110] The system determines the current output brightness range of the lighting device and calls the pre-trained nonlinear mixing model for that range. This nonlinear mixing model establishes a nonlinear mapping relationship between multi-dimensional input vectors (target color temperature, brightness range, LED junction temperature, drive current deviation, etc.) and the PWM duty cycle of the warm and cool light channels. Trained based on historical experimental data, it solves the nonlinearity problem of LED luminous characteristics by fitting the color temperature duty cycle curves of the LED under different operating conditions, achieving precise conversion from target color temperature to driving parameters.

[0111] LED junction temperature data is collected, driving current feedback signals of the warm and cool light channels are obtained, and combined with preset spectral ratio parameters, an input vector containing the target color temperature value, brightness range, LED junction temperature value, and driving current deviation is constructed based on the target color temperature value of the color temperature gradient adjustment curve, providing multi-source data support for subsequent duty cycle calculation.

[0112] The forward propagation calculation is performed using a nonlinear mixing model to generate the basic PWM duty cycle for the warm and cool light channels. If the output brightness of the current lighting device belongs to the low brightness range, the correction coefficient of the duty cycle of the cool light channel is generated by looking up the pre-stored offset compensation table or calling the nonlinear correction function, and the color temperature offset compensation is performed to adjust the basic PWM duty cycle.

[0113] Based on the collected LED junction temperature data, a pre-calibrated temperature and color temperature drift curve is invoked to generate a dynamic temperature compensation coefficient. The temperature and color temperature drift curve reflects the color temperature shift corresponding to each unit degree change in LED junction temperature. The dynamic temperature compensation coefficient is weighted by a PID controller to adjust the base PWM duty cycle of the warm and cool light channels in real time. For example, when the junction temperature rises, the duty cycle of the cool light channel is automatically increased to offset the decreasing color temperature trend, with a response time controlled in milliseconds, forming a real-time suppression mechanism for junction temperature fluctuations.

[0114] A high-precision current sampling resistor is used to obtain the actual driving current of the warm and cool light channels in real time, and the current deviation is calculated by comparing it with the preset current value. The preset current value is determined by the rated operating current of the lighting device and the safe current calculated by thermal simulation. When the current deviation exceeds the preset current deviation threshold, the adaptive control algorithm is activated. The current deviation is corrected in real time by adjusting the duty cycle of the basic PWM, which suppresses the influence of current ripple on color temperature, maintains the stability of the driving current, and ensures that the LED operates in the optimal current range.

[0115] An aging model is pre-established based on the LED light decay characteristics to predict the luminous flux decay curve of LEDs under different operating conditions. Based on these characteristics and combined with the cumulative operating time and historical light decay data of the lighting device, the aging model calculates the current degree of light decay and obtains the corresponding luminous flux compensation coefficient to compensate for the luminous flux decay and maintain the long-term stability of the target color temperature. An update cycle is configured to periodically update the luminous flux compensation coefficient to adjust the basic PWM duty cycle of the warm and cool light channels, gradually increasing the output power to offset the luminous flux decay, maintain the long-term stability of the target color temperature, and avoid the accumulation of color temperature drift caused by light decay.

[0116] The actual output color temperature of the lighting device is monitored in real time by the built-in spectral sensor and compared with the target color temperature value. The deviation data between the actual output color temperature and the target color temperature value is calculated. If it is greater than the preset color temperature deviation threshold, a secondary correction mechanism is triggered: the current input vector is updated, a corrected PWM duty cycle is generated, and the deviation is gradually reduced through multi-cycle feedback adjustment to ensure that the output color temperature is stable within the target range.

[0117] Example 2:

[0118] Please see Figure 6 This embodiment introduces an adjustable color temperature lighting device, including a perception and interaction module, an execution and decision-making module, and an intelligent control module:

[0119] The perception and interaction module is used to collect ambient light data and receive user control commands; the execution and decision module is used to classify and judge the complexity of ambient light based on ambient light data, determine the initial target color temperature in combination with user control commands, and generate an appropriate color temperature gradient adjustment curve; the intelligent control module is used to dynamically calculate and correct the deviation of the driving parameters of the lighting device according to the color temperature gradient adjustment curve, and to control the color temperature.

[0120] The perception and interaction module comprises an ambient light acquisition unit, a user interaction unit, and a data preprocessing unit. The ambient light acquisition unit constructs a multi-layered light signal acquisition architecture by integrating a spectral color temperature sensor. The bottom layer deploys composite photosensitive elements covering the entire visible light spectrum to capture raw light signals; the middle layer uses adaptive filtering circuits to suppress environmental interference; and the top layer runs a light feature extraction algorithm to analyze the frequency of color temperature changes and illuminance fluctuations. It also supports dynamic adjustment of the light signal sampling frequency to adapt to different lighting environments and ensure acquisition accuracy. The user interaction unit integrates a capacitive touch panel and a far-field voice recognition component, allowing users to adjust the color temperature gradient, select scene modes such as meeting, reading, and sleep via gestures, and force color temperature values ​​via far-field voice input. It also interconnects with the smart home system through a communication module, synchronizing user-preset parameters to the edge control unit. The data preprocessing unit removes invalid information from the acquired raw ambient light data, performs priority management on multi-user control commands to avoid conflicts, and finally transmits the processed ambient light data and user control commands to the execution decision module.

[0121] The execution decision module includes a feature extraction unit, a complexity judgment unit, a target color temperature determination unit, and an adjustment curve generation unit. The feature extraction unit receives color temperature and illuminance value sequences from the perception interaction module, aligns them in the time dimension using a timestamp calibration algorithm, cleans the data using a filtering algorithm, and performs linear interpolation to repair outliers. It configures an evaluation time window to filter recent data and constructs an evaluation dataset. It calculates the color temperature abrupt change slope using a sliding window algorithm and calculates the illuminance fluctuation coefficient by combining the mean and standard deviation of illuminance, thus quantifying the illumination features in both the time and frequency domains. The complexity judgment unit configures color temperature abrupt change thresholds, illuminance fluctuation thresholds, and complexity thresholds. It performs a preliminary ambient light complexity judgment by statistically analyzing the proportion of abrupt changes and fluctuation windows in the evaluation dataset. For scenes initially determined to be low-complexity, it retrieves historical ambient light datasets with the same date type and timestamp, compares the matching degree between the current ambient light data and the historical fluctuation range, completes deep complexity calibration, and determines the final complexity level. The target color temperature determination unit parses user control commands and determines the initial target color temperature according to priority logic. If a mandatory color temperature value exists, it is used as the initial target color temperature. If the deviation from the standard color temperature value of the corresponding time period in the time scene color temperature benchmark library exceeds the warning threshold, a multi-dimensional color temperature warning is triggered. If a scene mode command exists, the preset color temperature of the corresponding scene is retrieved from the local strategy library. If no scene mode command exists, the standard color temperature of the current time period is retrieved from the time scene color temperature benchmark library. The adjustment curve generation unit initiates a graded control strategy according to the complexity level: for low-complexity scenes, a simple target matching strategy is used, which calculates environmental compensation parameters based on the illuminance deviation within a smooth period to generate a dynamic target color temperature. A color temperature adjustment sequence is generated according to the relationship between the color temperature difference and the graded threshold. After interpolation optimization and low-pass filtering, a color temperature gradient adjustment curve is generated. For high-complexity scenes, a dynamic target matching strategy is used. The periodicity of ambient light changes is judged through autocorrelation analysis, and a dynamic target color temperature is generated by combining periodic characteristics or abrupt change buffering mechanisms. After interpolation and filtering, a color temperature gradient adjustment curve is generated and transmitted to the intelligent control module.

[0122] The intelligent control module consists of a parameter calculation unit, a deviation correction unit, and a stable output unit. The parameter calculation unit first divides the output brightness of the lighting device into three intervals—low brightness, medium brightness, and high brightness—based on the nonlinear optical characteristics of the LED and the color temperature shift law. It then determines the interval to which the current brightness belongs and calls the corresponding pre-trained nonlinear mixing model. It collects LED junction temperature data and the current feedback signal driven by the warm and cool light channels, and combines this with preset spectral ratio parameters and the target color temperature value of the color temperature gradient adjustment curve to construct a multi-dimensional input vector. Through forward propagation of the nonlinear mixing model, it calculates the basic PWM duty cycle of the warm and cool light channels and compensates for the color temperature shift in the duty cycle of the low brightness interval. The deviation correction unit includes a temperature correction subunit, a current correction subunit, and a light decay correction subunit. The temperature correction subunit uses a pre-calibrated temperature and color temperature drift curve to generate a dynamic temperature compensation coefficient, and adjusts the PWM duty cycle in real time via a PID controller to offset the effects of junction temperature changes. The current correction subunit compares the actual drive current with a preset current value; when the current deviation exceeds a preset current deviation threshold, an adaptive control algorithm is activated to correct the PWM duty cycle and suppress current ripple interference. The light decay correction subunit calculates the luminous flux compensation coefficient based on an LED aging model, combined with accumulated running time and historical light decay data, and periodically updates the PWM duty cycle to offset color temperature drift caused by light decay. The stable output unit monitors the actual output color temperature in real time using a built-in spectral sensor, compares it with the target color temperature to calculate the color temperature deviation, and if the color temperature deviation exceeds a preset color temperature deviation threshold, a secondary correction mechanism is triggered to re-update the input vector and generate a corrected PWM duty cycle. Multi-cycle feedback adjustment ensures the lighting device maintains a stable output of the target color temperature over the long term.

[0123] Working principle and its effects:

[0124] This invention achieves precise adaptive control of adjustable color temperature lighting through ambient light sensing, intelligent hierarchical regulation, and multi-parameter fusion driving technology. In terms of ambient light processing, a sliding window algorithm is employed to extract the slope of color temperature abrupt changes and the illuminance fluctuation coefficient in real time. Simultaneously, timestamp calibration, data cleaning, and interpolation repair techniques are used to ensure data accuracy. Combined with historical data time-matching verification, the complexity of ambient light is dynamically quantified, accurately identifying complex lighting environments such as sudden changes in sunlight and switching of lighting, laying the foundation for intelligent regulation.

[0125] In terms of control strategy, differentiated control is implemented. For low-complexity scenarios, the initial target color temperature is compensated based on the illuminance deviation ratio. Environmental compensation parameters are calculated by comparing the mean within the smoothing period to quickly achieve basic adjustment. For high-complexity scenarios, the periodicity of ambient light is analyzed using the autocorrelation function, the delay period is marked, and a look-ahead compensation mechanism is activated. The direction and magnitude of compensation are determined by fitting historical data trends, achieving predictive smooth adjustment of color temperature and effectively solving the response lag problem of traditional devices.

[0126] In the drive control stage, the device constructs a multi-dimensional input vector including target color temperature, brightness range, LED junction temperature, and current deviation. A nonlinear light mixing model is used to establish a mapping relationship with the PWM duty cycle, and a PID controller is employed to correct the drive parameters in real time. To address issues such as color temperature shift, junction temperature variation, current fluctuation, and light decay in the low brightness range, techniques such as duty cycle correction, temperature compensation, adaptive current adjustment, and aging model compensation are employed to significantly improve the long-term stability of the lighting.

[0127] In addition, the device deeply integrates user-mandated color temperature settings and scene mode requirements, and combines a time-scene color temperature benchmark library with a local strategy library. While meeting personalized needs, it dynamically optimizes according to real-time changes in ambient light, achieving adaptive matching between lighting color temperature and environment and user needs, and providing innovative solutions for smart lighting.

[0128] The above description is merely a preferred embodiment of the present invention. The scope of protection of the present invention is not limited to the above embodiments. All technical solutions falling within the scope of the present invention's concept are within the scope of protection of the present invention. It should be noted that for those skilled in the art, any improvements and modifications made without departing from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A control method for an adjustable color temperature lighting device, characterized in that, include: Collect ambient light data and receive user control commands. The ambient light data includes color temperature data and illuminance data. The user control commands include forced color temperature value and scene mode. Based on the ambient light data acquisition time, an evaluation dataset is constructed and a preliminary ambient light complexity assessment is performed. By dividing the data into sliding windows, the slope of color temperature change and the illuminance fluctuation coefficient are extracted, abnormal sliding windows are identified, and the ambient light complexity is quantified based on the proportion of abnormal sliding windows to determine the current ambient light complexity. If the current ambient light complexity is determined to be low, then the depth complexity is evaluated based on the time matching degree of the evaluation dataset to determine whether the current ambient light complexity should be adjusted to high complexity. The initial target color temperature is obtained based on user control commands, and a hierarchical control strategy is initiated for the initial target color temperature according to the complexity of ambient light: If the ambient light complexity is low, a simple target matching strategy is adopted, and the initial target color temperature is dynamically compensated based on the illuminance deviation ratio to generate a color temperature gradient adjustment curve. If the ambient light complexity is high, a dynamic target matching strategy is adopted, and the abrupt change analysis of the ambient light complexity is performed to determine whether the ambient light change is periodic. Based on the initial target color temperature, dynamic adjustment is performed to generate a color temperature gradient adjustment curve. Based on the color temperature gradient adjustment curve, the driving parameters of the lighting device are dynamically calculated and deviation corrected to control the color temperature. The specific steps for evaluating depth complexity by combining the time matching degree of the evaluation dataset include: If the current ambient light complexity is determined to be low, configure the historical data collection cycle and extract historical ambient light datasets with the same date type and the same timestamp; Statistical analysis was performed on historical ambient light datasets to calculate the mean and standard deviation of color temperature and illuminance, and to obtain the fluctuation range of color temperature and illuminance. The average color temperature and average illuminance of the evaluation dataset are compared with the fluctuation range. If they are within the fluctuation range, the time matching degree is determined to be satisfactory; otherwise, the time matching degree is determined to be abnormal. If the time matching is determined to be abnormal, the current ambient light complexity is adjusted from low complexity to high complexity.

2. The control method for an adjustable color temperature lighting device as described in claim 1, characterized in that, The specific steps of the dynamic target matching strategy include: Perform periodic analysis on the color temperature and illuminance value sequences to determine whether the changes in ambient light are periodic and mark the delay period; If it is determined to be periodic, the adjustment factor parameter is configured, the look-ahead compensation mechanism is activated according to the delay period, and the advance adjustment time of the look-ahead compensation is calculated based on the delay period and the adjustment factor parameter. Configure a historical delay sliding window, fit a linear trend line to the color temperature change sequence within the historical delay sliding window, calculate the color temperature change rate per unit time, and determine the compensation direction for the current delay period. Configure the fluctuation analysis window, calculate the average color temperature value within the fluctuation analysis window, and use the average color temperature fluctuation within the delay period as the compensation amount to correct the initial target color temperature based on the determined compensation direction, thus serving as the dynamic target color temperature. Calculate the color temperature difference between the actual output color temperature of the current lighting device and the dynamic target color temperature, decompose the advance adjustment time into equally spaced time points to form an adjustment time axis, perform smooth transition processing on the current color temperature value and the dynamic target color temperature, generate a color temperature adjustment sequence and perform smoothing processing, and generate a color temperature gradient adjustment curve. If the color temperature is determined to be non-periodic, a mutation buffer mechanism is triggered to set a cooling-off period. During the cooling-off period, the color temperature value sequence is processed by moving average to extract the color temperature trend value. If the color temperature trend value deviates from the initial target color temperature by more than a preset smoothing threshold, a slow adjustment strategy is initiated. A dynamic target color temperature is generated through weighted fusion, and then a color temperature gradient adjustment curve is generated through linear interpolation and filtering.

3. The control method for an adjustable color temperature lighting device as described in claim 1, characterized in that, The specific steps for conducting a preliminary assessment of ambient light complexity include: A color temperature value sequence and an illuminance value sequence are constructed based on the acquisition time of ambient light data, and the color temperature value sequence and the illuminance value sequence are aligned in the time dimension using a timestamp calibration algorithm; The color temperature value sequence and the illuminance value sequence are filtered and cleaned, outlier identified, and linear interpolation repaired. Configure the evaluation time window, filter the data of color temperature value sequence and illuminance value sequence within the evaluation time window before the current timestamp, and construct the evaluation dataset; Set a sliding window and a sliding step size, calculate the color temperature difference between adjacent data points in the color temperature value sequence within the sliding window and the slope per unit time, and obtain the color temperature change slope of the sliding window based on the slope per unit time within the sliding window; Calculate the mean and standard deviation of the illuminance value sequence within the sliding window, and obtain the illuminance fluctuation coefficient by calculating the fluctuation coefficient within the sliding window; Configure color temperature abrupt change threshold and illuminance fluctuation threshold, and mark the color temperature abrupt change window and illuminance fluctuation window according to the color temperature abrupt change slope and illuminance fluctuation coefficient of the sliding window; Configure complexity thresholds and statistically evaluate the proportion of color temperature abrupt change windows and illuminance fluctuation windows within the dataset to determine the current ambient light complexity, including low complexity and high complexity.

4. The control method for an adjustable color temperature lighting device as described in claim 1, characterized in that, The specific steps of the simple target matching strategy include: Configure a smoothing period, extract the average color temperature and average illuminance within the smoothing period of the current timestamp, if the deviation between the current illuminance value and the average illuminance value is greater than the illuminance deviation threshold, then determine the ambient light deviation and calculate the product of the illuminance deviation ratio and the average color temperature as the environmental compensation parameter, and generate a dynamic target color temperature by weighted fusion of the initial target color temperature and the environmental compensation parameter. Configure a grading threshold and calculate the color temperature difference between the actual output color temperature of the current lighting device and the dynamic target color temperature. If the color temperature difference is greater than the grading threshold, a phased adjustment mode is adopted, and the adjustment stages are divided according to the proportion of the color temperature difference. Each stage sets the single adjustment amplitude according to the preset adjustment gradient to generate a color temperature adjustment sequence; otherwise, the preset adjustment gradient is used to generate a color temperature adjustment sequence. The smoothing cycle duration is decomposed into equally spaced time points. Based on the total amplitude of the color temperature adjustment sequence and the time axis, the color temperature adjustment sequence is optimized and smoothed to generate a color temperature gradient adjustment curve.

5. The control method for an adjustable color temperature lighting device as described in claim 1, characterized in that, The specific steps for obtaining the initial target color temperature based on user control commands include: Parse the user control command and determine if there is a forced color temperature setting. If there is, use the forced color temperature value as the initial target color temperature. Configure warning thresholds, retrieve the standard color temperature value for the corresponding time period from the time scene color temperature benchmark library based on the current timestamp, calculate the absolute value of the deviation between the standard color temperature value and the forced color temperature value, and trigger a multi-dimensional color temperature warning if the absolute value of the deviation is greater than the warning threshold. If no forced color temperature setting exists, determine whether a scene mode is set. If a scene mode is set, retrieve the scene color temperature corresponding to the current scene mode from the local policy library as the initial target color temperature. If no scene mode is set, the standard color temperature value for the corresponding time period will be retrieved from the time scene color temperature reference library based on the current timestamp as the initial target color temperature.

6. The control method for an adjustable color temperature lighting device as described in claim 2, characterized in that, The specific steps for determining whether changes in ambient light are periodic include: Configure an autocorrelation window to extract subsequences of color temperature and illuminance values ​​within the autocorrelation window prior to the current timestamp; Given a time delay sequence, for the color temperature value subsequence and the illuminance value subsequence, at each time delay point of the time delay sequence, calculate the autocorrelation coefficient using the autocorrelation function to generate an autocorrelation coefficient sequence; Configure a periodic threshold, traverse the autocorrelation coefficient sequence, and determine whether the ambient light change is periodic by judging whether the autocorrelation coefficient corresponding to the time delay is greater than the periodic threshold. If it is periodic, select the time delay as the delay period.

7. The control method for an adjustable color temperature lighting device as described in claim 1, characterized in that, The specific steps for dynamically calculating and correcting the deviation of the driving parameters of the lighting device and adjusting the color temperature include: The output brightness of the lighting device is divided into intervals, and the interval to which the current output brightness of the lighting device belongs is determined, including low brightness interval, medium brightness interval and high brightness interval, and the nonlinear light mixing model of the corresponding interval is called. The nonlinear mixing model is used to establish a nonlinear mapping relationship between the input vector and the PWM duty cycle of the warm and cool light channels. The elements of the input vector include: target color temperature value, brightness range, LED junction temperature value, and drive current deviation. LED junction temperature data is collected, driving current feedback signals of the warm and cool light channels are obtained, and input vectors are constructed based on the target color temperature value of the color temperature gradient adjustment curve, combined with preset spectral ratio parameters. A nonlinear light mixing model is used to perform forward propagation calculations to generate the basic PWM duty cycle for the warm and cold light channels.

8. The control method for an adjustable color temperature lighting device as described in claim 7, characterized in that, The specific steps for dynamically calculating and correcting the deviation of the driving parameters of the lighting device and adjusting the color temperature also include: If the output brightness of the current lighting device belongs to the low brightness range, a correction coefficient for the duty cycle of the cold light channel is generated to compensate for the color temperature shift and adjust the basic PWM duty cycle. Based on the collected LED junction temperature data, the pre-calibrated temperature and color temperature drift curve is called to generate a dynamic temperature compensation coefficient. The dynamic temperature compensation coefficient is weighted by the PID controller to adjust the basic PWM duty cycle. The actual driving current of the warm and cold light channels is obtained and compared with the preset current value to calculate the current deviation. When the current deviation exceeds the preset current deviation threshold, the adaptive control algorithm is activated to correct the current deviation in real time by adjusting the duty cycle of the basic PWM. An aging model is pre-established based on the LED light decay characteristics. Combined with the cumulative operating time of the lighting device and historical light decay data, the current light decay level is calculated, the corresponding luminous flux compensation coefficient is obtained, the update cycle is configured, and the luminous flux compensation coefficient is updated periodically to adjust the basic PWM duty cycle. The actual output color temperature of the lighting device is monitored in real time and compared with the target color temperature value. The color temperature deviation between the actual output color temperature and the target color temperature value is calculated. If it is greater than the preset color temperature deviation threshold, a secondary correction mechanism is triggered to correct the PWM duty cycle.

9. An adjustable color temperature lighting device, controlled by a control method for an adjustable color temperature lighting device according to any one of claims 1-8, characterized in that, It includes a perception and interaction module, an execution and decision-making module, and an intelligent control module: The perception and interaction module is used to collect ambient light data and receive user control commands; the execution decision module is used to classify and judge the ambient light complexity based on the ambient light data, determine the initial target color temperature in combination with the user control commands, and generate a color temperature gradient adjustment curve; the intelligent control module is used to dynamically calculate and correct the deviation of the driving parameters of the lighting device according to the color temperature gradient adjustment curve, and perform color temperature control.

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