Lighting device capable of adjusting color temperature and control method thereof

Abstract

The invention discloses a color temperature adjustable lighting device and a control method thereof, and belongs to the technical field of intelligent lighting. The invention aims to solve the problems that the existing color temperature adjustable lighting device is difficult to adapt to complex ambient light change, the individual demand fusion of users is insufficient and the color temperature stability is poor. Ambient light and a user control instruction are collected through a perception interaction module, an execution decision module constructs an evaluation data set based on ambient light collection time, ambient light complexity is quantized through multi-dimensional feature extraction and deep evaluation is carried out, after initial target color temperature is obtained in combination with the user control instruction, a hierarchical control strategy is started according to the complexity level, and a target color temperature is obtained. Generating a color temperature gradient adjustment curve; and the intelligent control module performs dynamic calculation and deviation correction on the driving parameters, thereby realizing accurate regulation and control of the color temperature. According to the scheme, the adaptive matching capability and the long-term stability of the illumination color temperature, the environment and the user demand are improved through the environment light complexity evaluation and hierarchical control strategy.

Description

Technical Field

[0001] The present invention relates to the field of intelligent lighting technology, and more particularly to a lighting device with adjustable color temperature and a control method thereof. Background Art

[0002] In the field of smart lighting, lighting devices with adjustable color temperature have been widely used in homes, offices and public spaces. In the existing technology, traditional adjustable color temperature lighting devices mostly rely on fixed parameter settings or simple ambient light sensing, which makes it difficult to cope with complex and changing light environment requirements. On the one hand, some devices can only adjust based on a single illuminance or color temperature data. When the ambient light has sudden changes in color temperature or high-frequency fluctuations, such as the periodic changes in sunlight through blinds and the frequent switching of external lights, they cannot accurately identify the complexity of the ambient light, resulting in delayed or over-response in color temperature adjustment, affecting visual comfort; on the other hand, existing devices have limited ability to integrate user personalized needs. For example, after the user sets a specific scene mode, it is difficult to dynamically optimize the color temperature parameters in combination with real-time ambient light, and it is easy to mismatch with the ambient light. In addition, during long-term operation, affected by LED light decay, junction temperature changes and grid voltage fluctuations, traditional devices lack an effective color temperature drift suppression mechanism, resulting in poor output color temperature stability and shortened service life. These problems make it difficult for existing color temperature adjustable lighting devices to balance environmental adaptability, user personalized needs and long-term stability in practical applications. Therefore, in order to overcome these limitations, the present invention proposes a color temperature adjustable lighting device and a control method thereof. Summary of the Invention

[0003] In response to the shortcomings of the existing technology, the purpose of the present invention is to provide a lighting device with adjustable color temperature and a control method thereof, which is committed to solving the problem in the existing technology that lighting devices are difficult to accurately, dynamically and stably adjust the color temperature according to complex and changeable ambient light, such as sudden changes in color temperature, fluctuations in illumination and personalized user needs. Through technical means such as ambient light complexity assessment, hierarchical control strategy, dynamic correction of driving parameters, etc., adaptive matching of lighting color temperature with the environment and user needs is achieved, thereby improving lighting comfort and stability.

[0004] To achieve the above object, the present invention provides the following technical solutions: A method for controlling a lighting device with adjustable color temperature, comprising: Collect ambient light data and receive user control instructions. The ambient light data includes color temperature value data and illuminance value data. The user control instructions include forced color temperature value and scene mode. Based on the ambient light data collection time, an evaluation dataset is constructed and a preliminary ambient light complexity assessment is performed. By partitioning the data through sliding windows, the color temperature mutation slope and 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, a depth complexity evaluation is performed based on the temporal matching of the evaluation data set to determine whether the current ambient light complexity should be adjusted to high. The initial target color temperature is obtained based on the user control instruction, and a hierarchical control strategy is initiated for the initial target color temperature according to the complexity of the ambient light: If the ambient light complexity is low, a simple target matching strategy is used to dynamically compensate 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 used to perform mutation analysis on the ambient light complexity to determine whether the ambient light changes are periodic. Dynamic regulation is then performed based on the initial target color temperature to generate a color temperature gradient adjustment curve. According to the color temperature gradient adjustment curve, the driving parameters of the lighting device are dynamically calculated and the deviation is corrected to adjust the color temperature.

[0005] Specifically, the steps of the dynamic target matching strategy include: Perform periodic analysis on the color temperature value sequence and the illuminance value sequence to determine whether the ambient light change is periodic and mark the delay period; If it is determined to be periodic, the adjustment factor parameters are configured, and the forward compensation mechanism is activated according to the delay period. The advance adjustment time of the forward compensation is calculated based on the delay period and the adjustment factor parameters. 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 a fluctuation analysis window, calculate the average color temperature value within the fluctuation analysis window, and use the average color temperature fluctuation value within the delay period as the compensation amount according to the determined compensation direction to correct the initial target color temperature and obtain the dynamic target color temperature. Calculate the color temperature difference between the actual output color temperature value 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 timeline, perform smooth transition processing on the current color temperature value and the dynamic target color temperature, generate a color temperature adjustment sequence, and perform smooth processing to generate a color temperature gradient adjustment curve; If it is determined that there is no periodicity, the mutation buffer mechanism is triggered to set a cooling-off period. The color temperature value sequence within the cooling-off period is processed by moving average to extract the color temperature trend value. If the deviation between the color temperature trend value and the initial target color temperature exceeds the preset smoothing threshold, the slow adjustment strategy is initiated to generate a dynamic target color temperature through weighted fusion, and then generate a color temperature gradient adjustment curve through linear interpolation and filtering.

[0006] Specifically, the specific steps for performing a preliminary ambient light complexity assessment include: Construct a color temperature value sequence and an illuminance value sequence based on the acquisition time of the ambient light data, and align the color temperature value sequence and the illuminance value sequence in the time dimension using a timestamp calibration algorithm; Perform filtering and cleaning, outlier identification, and linear interpolation repair on color temperature value sequences and illuminance value sequences; Configure the evaluation time window, filter the data of the color temperature value sequence and illuminance value sequence within the evaluation time window before the current timestamp, and build the evaluation data set; Set the sliding window and sliding step size, calculate the color temperature difference and unit time slope of adjacent data points of the color temperature value sequence in the sliding window, and obtain the color temperature mutation slope of the sliding window based on the unit time slope in 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 the color temperature mutation threshold and illuminance fluctuation threshold, and mark the color temperature mutation window and illuminance fluctuation window according to the color temperature mutation slope and illuminance fluctuation coefficient of the sliding window; Configure the complexity threshold and statistically evaluate the proportion of color temperature mutation windows and illuminance fluctuation windows in the dataset to determine the current ambient light complexity, including low complexity and high complexity.

[0007] Specifically, the specific steps for evaluating the depth complexity by combining the temporal matching of the evaluation dataset include: If the current ambient light complexity is determined to be low, configure the historical data collection cycle and extract the historical ambient light dataset with the same date type and the same timestamp; Perform statistical analysis on historical ambient light data sets, calculate the mean and standard deviation of color temperature and illuminance, and obtain the fluctuation range of color temperature and illuminance; Compare the color temperature mean and illuminance mean of the evaluation data set with the fluctuation range. If they are within the fluctuation range, the time matching is considered to be up to standard; otherwise, the time matching is considered 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.

[0008] Specifically, the specific steps of the simple target matching strategy include: Configure a smoothing cycle and extract the mean color temperature and illuminance values ​​within the smoothing cycle of the current timestamp. If the deviation between the current illuminance value and the mean illuminance value is greater than the illuminance deviation threshold, determine the ambient light deviation and calculate the product of the illuminance deviation ratio and the mean color temperature value as the environmental compensation parameter. 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 value of the current lighting device and the dynamic target color temperature. If the color temperature difference is greater than the grading threshold, a staged adjustment mode is adopted. The number of adjustment stages is divided according to the ratio of the color temperature difference. The single adjustment range of each stage is set 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. According to the total amplitude and time axis of the color temperature adjustment sequence, the color temperature adjustment sequence is optimized and smoothed to generate a color temperature gradient adjustment curve.

[0009] Specifically, the specific steps of obtaining the initial target color temperature based on the user control instruction include: Parse the user control command to determine whether there is a forced color temperature setting. If so, use the forced color temperature value as the initial target color temperature; Configure the warning threshold, retrieve the standard color temperature value of the corresponding time period from the time scene color temperature benchmark library according to 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 there is no forced color temperature setting, determine whether to set a scene mode. 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 the scene mode is not set, the standard color temperature value of the corresponding time period is retrieved from the time scene color temperature reference library according to the current timestamp as the initial target color temperature.

[0010] Specifically, the steps of determining whether the ambient light change is periodic include: Configure the autocorrelation window and intercept the color temperature value subsequence and illuminance value subsequence within the autocorrelation window before the current timestamp; A time delay sequence is set, and for the color temperature value subsequence and the illuminance value subsequence, an autocorrelation coefficient is calculated at each time delay point in the time delay sequence by using an 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.

[0011] Specifically, the specific steps of dynamically calculating and correcting the driving parameters of the lighting device and controlling the color temperature include: Divide the output brightness of the lighting device into intervals, determine the interval to which the output brightness of the current lighting device belongs, including a low brightness interval, a medium brightness interval, and a high brightness interval, and call a nonlinear light mixing model for the corresponding interval; The nonlinear light mixing model is used to establish a nonlinear mapping relationship between the input vector and the PWM duty cycle of the cold and warm light channels. The elements of the input vector include: target color temperature value, brightness range, LED junction temperature value, and drive current deviation; Collect LED junction temperature data, obtain the driving current feedback signal of the cold and warm light channels, and construct the input vector based on the target color temperature value of the color temperature gradient adjustment curve in combination with the preset spectral ratio parameters; The nonlinear light mixing model is used to perform forward propagation calculations to generate the basic PWM duty cycles of the cold and warm light channels.

[0012] Specifically, the specific steps of dynamically calculating and correcting the driving parameters of the lighting device and controlling 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 cold light channel duty cycle is generated to compensate for the color temperature offset 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 cold and warm 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 basic PWM duty cycle. An aging model is pre-established based on LED light decay characteristics. Combined with the accumulated operating time and historical light decay data of the lighting device, the current light decay level is calculated and the corresponding luminous flux compensation coefficient is obtained. The update cycle is configured and the luminous flux compensation coefficient is regularly updated to adjust the basic PWM duty cycle. The actual output color temperature of the lighting device is monitored in real time, compared with the target color temperature value, and 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, the secondary correction mechanism is triggered to correct the PWM duty cycle.

[0013] A lighting device with adjustable color temperature includes a perception and interaction module, an execution and decision module, and an intelligent control module: The perception and interaction module is used to collect ambient light data and receive user control instructions; the execution and decision-making module is used to make graded judgments on the complexity of ambient light based on the ambient light data, determine the initial target color temperature in combination with user control instructions, and generate a color temperature gradient adjustment curve; the intelligent control module is used to dynamically calculate and correct the deviations of the driving parameters of the lighting device according to the color temperature gradient adjustment curve to perform color temperature control.

[0014] Beneficial effects of the present invention: This invention collects ambient light data and user control instructions, applies sliding window algorithms, time matching evaluation, and other techniques to quantify ambient light complexity. Based on a hierarchical control strategy, it achieves precise responses to light environments of varying complexity. It utilizes a nonlinear light mixing model combined with multiple parameters such as LED junction temperature and current feedback to dynamically correct drive parameters, compensating for low-brightness offsets and long-term light decay. It also deeply integrates user personalized needs with scene modes. Overall, this solution effectively addresses the problems of traditional lighting devices, such as poor environmental adaptability, delayed color temperature adjustment, insufficient stability, and weak personalized integration. 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. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a flow chart of a control method for a lighting device with adjustable color temperature according to the present invention; Figure 2 This is a flow chart of the specific steps of determining the complexity of ambient light according to the present invention; Figure 3 This is a flow chart of the present invention for determining the complexity of ambient light in depth; Figure 4 A flowchart of the specific steps of obtaining the initial target color temperature of the lighting device according to the present invention; Figure 5 A flowchart of the specific steps of the dynamic target matching strategy of the present invention; Figure 6 The figure is a schematic structural diagram of a lighting device with adjustable color temperature according to the present invention. DETAILED DESCRIPTION

[0016] Example 1: See also Figure 1 This embodiment introduces a method for controlling a lighting device with adjustable color temperature, including: Step S1: Collect ambient light data, including color temperature value data and illuminance value data, through sensors, and receive user control instructions, including forced color temperature value and scene mode; so that the lighting device can adaptively trigger the control mode switching based on the stability characteristics of the ambient light and simultaneously receive user personalized needs.

[0017] In this embodiment, ambient light data collection and command reception are achieved by integrating a spectral color temperature sensor with a multimodal interaction component. The ambient light data sensing component utilizes a multi-layered optical signal acquisition architecture: a composite photosensitive element capable of covering the entire visible light band is deployed at the bottom layer, an adaptive filtering circuit is configured in the middle layer to suppress ambient light interference, and a light feature extraction algorithm is run at the top layer, enabling real-time analysis of the frequency of color temperature mutations and illuminance fluctuation trends. The user interaction component integrates a capacitive touch panel with far-field voice recognition. The touch interface supports gesture sliding to adjust the color temperature gradient and select scene modes such as meeting mode, reading mode, and sleep mode. The touch interface is interconnected with the smart home system via a communication module, synchronizing user preset parameters to the edge control unit, forming a bidirectional data input channel for ambient light characteristics and user preferences. By dynamically adjusting the optical signal sampling frequency and user control command priority management mechanism, the system ensures that ambient light stability characteristics can be accurately extracted and a personalized light demand model can be established even in scenarios with strong light interference or multi-user control.

[0018] Step S2: Based on the collected ambient light data, light features are extracted from the time and frequency domains to quantify the ambient light complexity. A preliminary ambient light complexity assessment is performed. If the ambient light complexity is determined to be low, a deeper ambient light complexity assessment is performed by combining temporal matching with the human body state. A hierarchical control strategy is initiated based on the ambient light complexity level to obtain a color temperature gradient adjustment curve for the lighting device. In this embodiment, by performing feature fusion on the ambient light data, the sliding window algorithm is used to calculate the slope of the color temperature mutation and the illuminance fluctuation coefficient, and the short-time Fourier transform is combined to identify high-frequency interference features to complete the preliminary complexity judgment. For scenes judged to be of low complexity, the time model constructed by the historical light data of the same time period is further called to calculate the real-time deviation rate to further perform complexity judgment. A hierarchical control strategy is initiated for different levels. The initial target color temperature of the lighting device is obtained: low-complexity scenes directly map the locally stored time and time-scene color temperature benchmark library, and superimpose the human body state fine-tuning coefficient; high-complexity scenes trigger cloud collaboration, generate dynamic color temperature curves based on the regional light environment database and real-time meteorological data, and optimize the ratio of cold and warm light in combination with the spectral fitting algorithm; See also Figure 2 Preferably, the specific steps of determining the complexity of ambient light include: According to the collection 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 through a timestamp calibration algorithm to eliminate the feature calculation deviation caused by asynchronous sampling, laying the foundation for subsequent spatiotemporal consistency analysis.

[0019] The color temperature value sequence and illuminance value sequence are filtered to clean the data, remove random noise, identify outliers, and perform linear interpolation repair on the outliers; ensure the continuity and authenticity of the input data, and provide a reliable data basis for subsequent analysis.

[0020] Configure an evaluation time window, filter the color temperature value sequence and illuminance value sequence data within the evaluation time window before the current timestamp, build an evaluation dataset, and perform a preliminary ambient light complexity assessment. By limiting the analysis scope, focus on the characteristics of recent light environment changes and avoid interference from historical data on real-time judgment.

[0021] The sliding window and sliding step size are set according to the basic dynamics of the scene. 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 color temperature mutation slope of the sliding window, highlighting the impact of significant change events.

[0022] The mean and standard deviation of the illuminance value sequence within the sliding window are calculated to calculate the fluctuation coefficient within the sliding window as the illuminance fluctuation coefficient of the sliding window, eliminating the absolute value differences under different illuminance levels.

[0023] Configure the color temperature mutation threshold and illuminance fluctuation threshold to identify abnormal sliding windows with significant light environment changes. If the color temperature mutation slope of the sliding window is greater than the color temperature mutation threshold, the sliding window is marked as a color temperature mutation window, indicating significant color dynamic changes. 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 significant brightness dynamic changes.

[0024] 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 mutation windows and illuminance fluctuation windows within the evaluation dataset. If the proportion of color temperature mutation 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, the current ambient light complexity is determined to be low. Otherwise, the current ambient light complexity is determined to be high.

[0025] See also Figure 3 If the current ambient light complexity is judged to be low, a deep ambient light complexity assessment is performed based on the temporal matching of the evaluation dataset. The complexity rating is further calibrated from the two dimensions of temporal regularity and human activity characteristics, and the ambient light complexity is deeply determined, namely: Configure the historical data collection cycle to systematically collect and store historical ambient light data. Extract historical ambient light data sets with the same date type and timestamp as the evaluation data set to serve as benchmark data for determining whether the current light environment conforms to temporal regularity. Date types include weekdays and weekends.

[0026] Perform statistical analysis on historical ambient light data sets, calculate the mean and standard deviation of color temperature and illuminance, and obtain the fluctuation range of color temperature and illuminance; The mean color temperature and illuminance of the evaluation dataset are calculated and compared with the color temperature and illuminance fluctuation ranges of the historical ambient light dataset. If they are within the fluctuation range, the temporal matching is considered satisfactory, indicating that the light environment changes conform to the historical patterns of that period. Otherwise, the temporal matching is considered abnormal, indicating that unexpected light environment changes may have occurred. If the temporal matching is abnormal, the current ambient light complexity is adjusted from low to high. This effectively identifies regular scenarios such as seasonal light changes and timed device linkage, reduces the false positive rate for low-complexity scenarios, and ensures a timely response to sudden abnormal light environments.

[0027] See also Figure 4 Preferably, the specific steps of obtaining the initial target color temperature of the lighting device include: The system analyzes received user control commands to determine whether a forced color temperature setting exists. If a forced color temperature value is set via the touch panel, voice command, or smart home system, the system uses that forced color temperature as the initial target color temperature and simultaneously configures a warning threshold based on the reasonable fluctuation range of the standard color temperature values ​​for the corresponding time period in the time scene color temperature benchmark library. The system respects the user's individual needs and prioritizes the color temperature explicitly set by the user.

[0028] Based on the current timestamp, the standard color temperature value for the corresponding time period is retrieved from the time scene color temperature reference library. The absolute deviation between the standard color temperature value and the mandatory color temperature value is calculated. If the absolute deviation exceeds the warning threshold, a color temperature warning is triggered. A color temperature anomaly alert is pushed via the mobile app, the local touch panel flashes a warning light, and a graphic message "Current color temperature deviates from the recommended range" is displayed on the lighting device's human-machine interface, reminding the user that the current color temperature may affect visual comfort or human rhythms.

[0029] If the user hasn't 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 and include scene color temperature, illuminance, and spectral ratio for specific scenarios. When a scene mode command is detected, the scene color temperature of the current scene mode is used as the initial target color temperature. This allows for quick matching of appropriate color temperature parameters to meet lighting requirements in different scenarios and enhance the user experience.

[0030] If no scene mode is set, the standard color temperature value of the corresponding time period is retrieved from the time scene color temperature benchmark library according to the current timestamp as the initial target color temperature; the time scene color temperature benchmark library integrates the standard color temperature values ​​of conventional color temperature references for different date types and time periods, and provides default and reasonable color temperature settings for scenes without clear user control instructions, ensuring basic lighting adaptability.

[0031] 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: Configure a smoothing cycle to stabilize the ambient light data within the current time period. The system extracts the mean color temperature and illuminance values ​​within the smoothing cycle for the time period corresponding to the current timestamp. If the deviation between the current illuminance value and the mean illuminance value is greater than the preset illuminance deviation threshold, the system determines that the ambient light has deviated. The product of the illuminance deviation ratio and the mean color temperature value is calculated as the ambient 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 ambient compensation parameter. Based on the 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 mean illuminance value is less than or equal to the preset illuminance deviation threshold, the system determines that the ambient light has not deviated, and the initial target color temperature is used as the dynamic target color temperature.

[0032] Configure a grading threshold, calculate the color temperature difference between the actual output color temperature value of the current lighting device and the dynamic target color temperature, and start the graded adjustment strategy according to the size of the color temperature difference. If the color temperature difference is greater than the grading threshold, adopt a staged adjustment mode, divide the number of adjustment stages according to the size ratio of the color temperature difference, and for each adjustment stage, set the single adjustment amplitude according to the preset adjustment gradient, generate the color temperature adjustment subsequence of the single adjustment stage, and finally obtain the color temperature adjustment sequence; otherwise, adopt the preset adjustment gradient, set the single adjustment amplitude, and generate the color temperature adjustment sequence; through graded adjustment, it can avoid the visual impact caused by sudden color temperature changes when the temperature difference is large, and can quickly complete fine-tuning when the temperature difference is small, so as to achieve precise control.

[0033] The smoothing cycle is broken down into equally spaced time points. Based on the total amplitude and time axis of the color temperature adjustment sequence, the color temperature adjustment sequence is optimized using linear interpolation or S-curve interpolation. A first-order low-pass filter is used to smooth the color temperature adjustment sequence and generate a gradual color temperature adjustment curve. This ensures a smooth and natural color temperature adjustment process, consistent with the human eye's adaptation to light changes and enhancing visual comfort.

[0034] See also Figure 5 If the ambient light complexity is determined to be high, the dynamic target matching strategy is used to eliminate unstable change factors. By performing mutation analysis on the ambient light complexity, it is determined whether the ambient light change is periodic. Dynamic control is performed based on the initial target color temperature to obtain a color temperature gradient adjustment curve. The specific steps of the dynamic target matching strategy include: Perform periodic analysis on the color temperature value sequence and the illuminance value sequence, configure the autocorrelation window according to the basic dynamics of the scene, and intercept the color temperature value subsequence and illuminance value subsequence within the autocorrelation window before the current timestamp to ensure that sufficient light signal fluctuation characteristics are included.

[0035] Autocorrelation analysis is performed on the color temperature and illuminance subsequences. The time delay sequence scan range is set, such as a continuous time interval from short to long delay, to traverse and analyze the correlation between the color temperature and illuminance subsequences at different time delays. The minimum and maximum intervals of the time delay sequence are dynamically adjusted based on the autocorrelation window duration to ensure coverage of the possible period intervals.

[0036] For the color temperature value subsequence and the illuminance value subsequence, at each time delay point in the time delay sequence, the autocorrelation coefficient between the signal and its delayed version is calculated using the autocorrelation function to generate a corresponding autocorrelation coefficient sequence, which quantifies the similarity between the color temperature value subsequence and the illuminance value subsequence at different delays. Configure a periodic threshold, traverse the autocorrelation coefficient sequence, and determine whether there is a color temperature value subsequence or illuminance value subsequence under a certain time delay with an autocorrelation coefficient greater than the periodic threshold. If so, the ambient light change under this time delay is determined to be periodic. The time delay that meets the conditions is marked as the delay period, which serves as the periodic characteristic parameter of the ambient light change. If there are multiple time delays that meet the conditions, 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. If it is determined that the ambient light changes are periodic, the adjustment factor parameters are configured based on the scene dynamics and the amplitude of the light environment change. For example, a larger factor is set for a rapidly changing scene and a smaller factor is set for a static scene. When it is determined that the ambient light changes are periodic, the forward compensation mechanism is started according to the delay period, and the advance adjustment time of the forward compensation is calculated according to the confirmed delay period combined with the adjustment factor parameters. For example, the delay period is multiplied by the adjustment factor parameter as the advance adjustment time, ensuring that the control is started before the periodic change of the light environment occurs, thereby realizing predictive adjustment; Configure a historical delay sliding window with a length that is an integer multiple of the delay period to ensure sufficient historical color temperature change data. Use a timestamp calibration algorithm to align the color temperature sequences for each period to eliminate analysis bias caused by time offsets.

[0037] For the color temperature change sequence within the historical delay sliding window, the least squares method is used to fit a linear trend line and calculate the color temperature change rate per unit time. If the slope is positive, it is determined that the color temperature has been increasing in the historical period; if the slope is negative, it is decreasing, which determines the compensation direction for the current delay period. Configure a fluctuation analysis window to cover the color temperature data of a single delay period. Calculate the average color temperature value within the fluctuation analysis window. Based on the determined compensation direction, use the average color temperature fluctuation value within the delay period as the compensation amount to correct the initial target color temperature and obtain the dynamic target color temperature. Calculate the color temperature difference between the actual output color temperature value of the current lighting device and the dynamic target color temperature, decompose the advance adjustment time into equally spaced time points according to the preset granularity to form an adjustment time axis, and use cubic spline interpolation or S-curve interpolation algorithm to perform smooth transition processing on the current color temperature and the dynamic target color temperature to generate a color temperature adjustment sequence; use a first-order low-pass filtering algorithm to smooth the color temperature adjustment sequence to generate a color temperature gradient adjustment curve.

[0038] If it is determined that the ambient light changes are not periodic, the mutation 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 lighting device from frequently oscillating in color temperature due to short-term fluctuations in ambient light when the ambient light changes are not periodic. Perform a moving average on the color temperature value sequence during the cool-down period 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 ambient light change is significant enough to require color temperature adjustment. In the slow adjustment strategy, a dynamic target color temperature is generated through weighted fusion based on the initial target color temperature and color temperature trend value. The cool-down 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 to generate a color temperature gradient adjustment curve.

[0039] Step S3: According to the color temperature gradient adjustment curve, the driving parameters are dynamically calculated and the deviation is corrected. By running the nonlinear light mixing model trained based on historical data and integrating the real-time collected LED junction temperature data and driving current feedback signal, the PWM duty cycle of the cold and warm light channels is optimized in real time, and the color temperature offset under low brightness is compensated. In addition, the driving parameters are corrected online through the temperature compensation coefficient and the grid fluctuation feedforward algorithm, so that the target color temperature remains stable during long-term operation of the lamp or voltage fluctuations, and the color temperature drift caused by light decay and junction temperature changes is suppressed.

[0040] In this embodiment, relying on a nonlinear light mixing model trained based on historical data, the real-time collected LED junction temperature data and the drive current feedback signal are deeply integrated. By dynamically calculating the PWM duty cycle of the cold and warm light channels, accurate tracking of the target color temperature is achieved. In particular, the color temperature offset problem in low-brightness scenes is targetedly corrected through the compensation mechanism built into the model. At the same time, with the help of the temperature compensation coefficient, the impact of LED junction temperature changes on color temperature is responded to in real time. During the long-term operation of the lamp, the drive parameters are continuously corrected online, 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 in different usage scenarios.

[0041] Preferably, the specific steps of dynamically calculating and correcting the driving parameters include: The output brightness of the lighting device is divided into intervals. These intervals are based on the nonlinear optical properties of LEDs at different currents and the color temperature shift patterns, resulting in low, medium, and high brightness intervals. This system combines the nonlinear perception of brightness by the human eye with the electrical and optical conversion characteristics of LED devices to ensure that the light mixing model within each interval is targeted. The current lighting device's output brightness is determined to be in a specific interval, and the corresponding nonlinear light mixing model is then invoked.

[0042] Determine the range to which the current lighting device's output brightness falls and invoke a pre-trained nonlinear light mixing model for the corresponding range. This nonlinear light mixing model establishes a nonlinear mapping relationship between multi-dimensional input vectors, such as the target color temperature, brightness range, LED junction temperature, and drive current deviation, and the PWM duty cycles of the warm and cold light channels. Trained based on historical experimental data, this model fits the color temperature-duty cycle curves of LEDs under different operating conditions, addressing the nonlinearity of LED luminous characteristics and achieving precise conversion from target color temperature to drive parameters.

[0043] Collect LED junction temperature data, obtain the driving current feedback signal of the cold and warm light channels, and combine it with the preset spectral ratio parameters. According to the target color temperature value of the color temperature gradient adjustment curve, construct an input vector including the target color temperature value, brightness range, LED junction temperature value, and driving current deviation, providing multi-source data support for subsequent duty cycle calculation.

[0044] A nonlinear light mixing model is used to perform forward propagation calculations to generate the basic PWM duty cycles of the cold and warm light channels. If the output brightness of the current lighting device falls within the low brightness range, a correction coefficient for the cold light channel duty cycle is generated by searching a pre-stored offset compensation table or calling a nonlinear correction function to perform color temperature offset compensation and adjust the basic PWM duty cycle. Based on the collected LED junction temperature data, a pre-calibrated temperature-color temperature drift curve is called to generate a dynamic temperature compensation coefficient. The temperature-color temperature drift curve reflects the color temperature offset corresponding to each unit degree change in LED junction temperature. A PID controller weights the dynamic temperature compensation coefficient and adjusts the base PWM duty cycle of the cold and warm light channels in real time. For example, when the junction temperature rises, the cold light channel's duty cycle is automatically increased to offset the trend of decreasing color temperature. The response time is controlled in milliseconds, forming a real-time suppression mechanism for junction temperature fluctuations.

[0045] High-precision current sampling resistors are used to obtain the actual drive current of the cold and warm light channels in real time, and then compare it with the preset current value to calculate the current deviation. The preset current value is determined by the lighting device's rated operating current and the safety current calculated by its thermal simulation. 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 basic PWM duty cycle, suppressing the impact of current ripple on color temperature, maintaining drive current stability, and ensuring that the LED operates within the optimal current range.

[0046] An aging model is pre-established based on LED light decay characteristics to predict the LED's lumen decay curve under different operating conditions. This aging model, based on the LED's lumen decay characteristics and combined with the lighting fixture's cumulative operating time and historical lumen decay data, calculates the current lumen decay level and, in turn, derives the corresponding lumen compensation coefficient to compensate for lumen decay and maintain the long-term stability of the target color temperature. A configured update cycle regularly updates the lumen compensation coefficient to adjust the base PWM duty cycle of the warm and cool light channels, gradually increasing output power to offset lumen decay, maintain the long-term stability of the target color temperature, and avoid the accumulation of color temperature drift caused by lumen decay.

[0047] The actual output color temperature of the lighting device is monitored in real time through the built-in spectral sensor, compared with the target color temperature value, and the deviation data of the actual output color temperature and the target color temperature value is calculated. If it is greater than the preset color temperature deviation threshold, the secondary correction mechanism is triggered: the current input vector is updated to generate a corrected PWM duty cycle. The deviation is gradually reduced through multi-cycle feedback adjustment to ensure that the output color temperature is stable within the target range.

[0048] Example 2: See also Figure 6 This embodiment introduces a lighting device with adjustable color temperature, including a perception and interaction module, an execution and decision module, and an intelligent control module: The perception and interaction module is used to collect ambient light data and receive user control instructions; the execution and decision-making module is used to make graded judgments on the complexity of ambient light based on the ambient light data, determine the initial target color temperature in combination with user control instructions, and generate an adaptive color temperature gradient adjustment curve; the intelligent control module is used to dynamically calculate and correct the driving parameters of the lighting device according to the color temperature gradient adjustment curve to perform color temperature control.

[0049] 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 integrates a spectral color temperature sensor to create a multi-level optical signal acquisition architecture. The bottom layer deploys a composite photosensitive element covering the entire visible light band to capture raw light signals. The middle layer employs an adaptive filtering circuit to suppress environmental interference. The top layer runs a light feature extraction algorithm to analyze the frequency of color temperature mutations and illuminance fluctuations. It also supports dynamic adjustment of the light signal sampling frequency to adapt to different lighting environments to ensure accurate acquisition. The user interaction unit integrates a capacitive touch panel and far-field voice recognition components. Users can adjust the color temperature gradient with gestures, select scene modes such as meeting, reading, and sleeping, and force color temperature values ​​through far-field voice input. The unit also connects to the smart home system via a communication module, synchronizing user preset parameters with the edge control unit. The data preprocessing unit removes invalid information from the collected raw ambient light data, prioritizes multiple user control commands to avoid conflicts, and ultimately transmits the processed ambient light data and user control commands to the execution decision module.

[0050] 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 the color temperature value sequence and illuminance value sequence transmitted by the perception interaction module, aligns the time dimension using a timestamp calibration algorithm, cleans the data using a filtering algorithm, and repairs outliers through linear interpolation. An evaluation time window is configured to filter recent data to construct an evaluation dataset. The color temperature mutation slope is calculated using a sliding window algorithm, and the illuminance fluctuation coefficient is calculated by combining the illuminance mean and standard deviation. This completes the quantification of lighting features in the time and frequency domains. The complexity judgment unit configures a color temperature mutation threshold, an illuminance fluctuation threshold, and a complexity threshold. A preliminary assessment of ambient light complexity is performed by statistically evaluating the percentage of mutation and fluctuation windows in the dataset. For scenes initially determined to be low in complexity, a historical ambient light dataset with the same date type and timestamp is retrieved. The matching degree between the current ambient light data and the historical fluctuation range is compared to complete deep complexity calibration and determine the final complexity level. The target color temperature determination unit analyzes user control commands and determines the initial target color temperature based on priority logic. If a mandatory color temperature value is present, it is used as the initial target color temperature. If the deviation from the standard color temperature value for the corresponding time period in the time scene color temperature reference library exceeds the warning threshold, a multi-dimensional color temperature warning is triggered. If a scene mode command is present, the preset color temperature for the corresponding scene in the local policy library is retrieved. If no scene mode command is present, the standard color temperature for the current time period is retrieved from the time scene color temperature reference library. The adjustment curve generation unit initiates a hierarchical control strategy based on the complexity level: Low-complexity scenarios use a simple target matching strategy. Environmental compensation parameters are calculated based on the illumination deviation within a smoothing period to generate a dynamic target color temperature. A color temperature adjustment sequence is generated based on the relationship between the color temperature difference and the grading threshold. A color temperature gradient adjustment curve is generated through interpolation optimization and low-pass filtering. High-complexity scenarios use a dynamic target matching strategy. Autocorrelation analysis is used to determine the periodicity of ambient light changes. A dynamic target color temperature is generated by combining periodic characteristics or a mutation buffer mechanism. A color temperature gradient adjustment curve is generated through interpolation and filtering, and transmitted to the intelligent control module.

[0051] 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 lighting device's output brightness into three ranges: low, medium, and high brightness, based on the LED's nonlinear optical characteristics and color temperature offset. It then determines the range to which the current brightness belongs and invokes the corresponding pre-trained nonlinear light mixing model. The module then collects LED junction temperature data and the current feedback signals driving the cold and warm light channels. Combining these with preset spectral matching parameters and the target color temperature value of the color temperature gradient adjustment curve, it constructs a multidimensional input vector. The nonlinear light mixing model is then forward-propagated to calculate the base PWM duty cycle of the cold and warm light channels, compensating for color temperature offset in the low-brightness range. The deviation correction unit comprises a temperature correction subunit, a current correction subunit, and a light decay correction subunit. The temperature correction subunit uses a pre-calibrated temperature-color temperature drift curve to generate a dynamic temperature compensation coefficient. It then uses a PID controller to adjust the PWM duty cycle in real time to offset the effects of junction temperature variations. The current correction subunit compares the actual drive current with the preset current value. When the current deviation exceeds the preset current deviation threshold, it initiates an adaptive control algorithm to correct the PWM duty cycle and suppress current ripple interference. The light decay correction subunit calculates the luminous flux compensation coefficient based on the LED aging model, cumulative operating time, and historical light decay data. It periodically updates the PWM duty cycle to offset color temperature drift caused by light decay. The stable output unit uses a built-in spectral sensor to monitor the actual output color temperature in real time, comparing it to the target color temperature and calculating the color temperature deviation. If the color temperature deviation exceeds the preset color temperature deviation threshold, a secondary correction mechanism is triggered, re-updating the input vector to generate a corrected PWM duty cycle. Multi-cycle feedback regulation ensures the lighting device maintains the long-term stability of the target color temperature.

[0052] Working principle and its effect: This invention achieves precise adaptation of adjustable color temperature lighting through ambient light sensing, intelligent hierarchical control, and multi-parameter fusion drive technology. For ambient light processing, a sliding window algorithm is used to extract the color temperature mutation slope and illumination fluctuation coefficient in real time. Timestamp calibration, data cleaning, and interpolation repair techniques are simultaneously applied to ensure data accuracy. Combined with historical data time matching verification, this method dynamically quantifies ambient light complexity and accurately identifies complex lighting environments such as sudden changes in daylight and lighting switching, laying the foundation for intelligent control.

[0053] In terms of control strategies, differentiated control is implemented. For low-complexity scenarios, the initial target color temperature is compensated based on the illuminance deviation ratio. The environmental compensation parameters are calculated by comparing the mean values ​​within a smoothing period, allowing for rapid basic adjustment. For high-complexity scenarios, the autocorrelation function is used to analyze the periodicity of ambient light, marking the delay period and activating a forward-looking compensation mechanism. The compensation direction and value are determined by combining historical data trend fitting, achieving predictive smooth adjustment of color temperature and effectively resolving the response lag problem of traditional devices.

[0054] In the drive control phase, the device constructs a multidimensional input vector containing the target color temperature, brightness range, LED junction temperature, and current deviation. It uses a nonlinear light mixing model to establish a mapping relationship with the PWM duty cycle, and uses a PID controller to correct the drive parameters in real time. To address issues such as color temperature drift, junction temperature variations, current fluctuations, and light decay in the low-brightness range, the device employs technologies such as duty cycle correction, temperature compensation, adaptive current regulation, and aging model compensation, significantly improving the long-term stability of the lighting.

[0055] In addition, the device deeply integrates user-forced color temperature settings and scene mode requirements, and combines the time scene color temperature benchmark library and the local policy library. While meeting personalized needs, it dynamically optimizes according to real-time changes in ambient light to achieve adaptive matching of lighting color temperature with the environment and user needs, providing innovative solutions for smart lighting.

[0056] 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 embodiment. All technical solutions based on the concept of the present invention are within the scope of protection of the present invention. It should be noted that for those skilled in the art, various improvements and modifications that do not depart from the principles of the present invention should also be considered within the scope of protection of the present invention.

Claims

1. A method for controlling a lighting device with adjustable color temperature, characterized in that: include: Collecting ambient light data and receiving user control instructions, the ambient light data including color temperature value data and illuminance value data, and the user control instructions including forced color temperature value and scene mode; Based on the ambient light data collection time, an evaluation dataset is constructed and a preliminary ambient light complexity assessment is performed. By partitioning the data through sliding windows, the color temperature mutation slope and 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 complexity, a depth complexity evaluation is performed in combination with the temporal matching of the evaluation data set to determine whether the current ambient light complexity should be adjusted to high complexity; The initial target color temperature is obtained based on the user control instruction, and a hierarchical control strategy is initiated for the initial target color temperature according to the complexity of the ambient light: If the ambient light complexity is low, a simple target matching strategy is adopted to dynamically compensate 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 to perform mutation analysis on the ambient light complexity to determine whether the ambient light changes are periodic. Dynamic regulation is performed on the basis of the initial target color temperature to generate a color temperature gradient adjustment curve. According to the color temperature gradient adjustment curve, the driving parameters of the lighting device are dynamically calculated and the deviation is corrected to perform color temperature control.

2. The method for controlling a lighting device with adjustable color temperature according to claim 1, wherein: The specific steps of the dynamic target matching strategy include: Perform periodic analysis on the color temperature value sequence and the illuminance value sequence to determine whether the ambient light change is periodic and mark the delay period; If it is determined to be periodic, the adjustment factor parameters are configured, and the forward compensation mechanism is activated according to the delay period. The advance adjustment time of the forward compensation is calculated based on the delay period and the adjustment factor parameters. 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 a fluctuation analysis window, calculate the average color temperature value within the fluctuation analysis window, and use the average color temperature fluctuation value within the delay period as the compensation amount according to the determined compensation direction to correct the initial target color temperature and obtain the dynamic target color temperature. Calculate the color temperature difference between the actual output color temperature value 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 timeline, perform smooth transition processing on the current color temperature value and the dynamic target color temperature, generate a color temperature adjustment sequence, and perform smooth processing to generate a color temperature gradient adjustment curve; If it is determined that there is no periodicity, the mutation buffer mechanism is triggered to set a cooling-off period. The color temperature value sequence within the cooling-off period is processed by moving average to extract the color temperature trend value. If the deviation between the color temperature trend value and the initial target color temperature exceeds the preset smoothing threshold, the slow adjustment strategy is initiated to generate a dynamic target color temperature through weighted fusion, and then generate a color temperature gradient adjustment curve through linear interpolation and filtering.

3. The method for controlling a lighting device with adjustable color temperature according to claim 1, wherein: The specific steps of performing preliminary ambient light complexity assessment include: Constructing a color temperature value sequence and an illuminance value sequence according to the acquisition time of the ambient light data, and aligning the color temperature value sequence and the illuminance value sequence in time dimension using a timestamp calibration algorithm; Performing filtering and cleaning, outlier identification, and linear interpolation repair on the color temperature value sequence and the illuminance value sequence; Configure the evaluation time window, filter the data of the color temperature value sequence and illuminance value sequence within the evaluation time window before the current timestamp, and build the evaluation data set; Setting a sliding window and a sliding step size, calculating the color temperature difference and the unit time slope of adjacent data points of the color temperature value sequence within the sliding window, and obtaining the color temperature mutation slope of the sliding window according to the unit time slope within the sliding window; Calculating the average value and standard deviation of the illuminance value sequence in the sliding window, and obtaining the illuminance fluctuation coefficient by calculating the fluctuation coefficient in the sliding window; Configure a color temperature mutation threshold and an illuminance fluctuation threshold, and mark the color temperature mutation window and the illuminance fluctuation window according to the color temperature mutation slope and the illuminance fluctuation coefficient of the sliding window; Configure the complexity threshold and statistically evaluate the proportion of color temperature mutation windows and illuminance fluctuation windows in the dataset to determine the current ambient light complexity, including low complexity and high complexity.

4. The method for controlling a lighting device with adjustable color temperature according to claim 1, wherein: The specific steps of performing depth complexity evaluation in combination with the temporal matching of the evaluation data set include: If the current ambient light complexity is determined to be low, configure the historical data collection cycle and extract the historical ambient light dataset with the same date type and the same timestamp; Perform statistical analysis on historical ambient light data sets, calculate the mean and standard deviation of color temperature and illuminance, and obtain the fluctuation range of color temperature and illuminance; Compare the color temperature mean and illuminance mean of the evaluation data set with the fluctuation range. If they are within the fluctuation range, it is determined that the time matching meets the standard; otherwise, it is determined that the time matching is abnormal. If the time matching is determined to be abnormal, the current ambient light complexity is adjusted from low complexity to high complexity.

5. The method for controlling a lighting device with adjustable color temperature according to claim 1, wherein: The specific steps of the simple target matching strategy include: Configure a smoothing cycle and extract the mean color temperature and illuminance values ​​within the smoothing cycle of the current timestamp. If the deviation between the current illuminance value and the mean illuminance value is greater than the illuminance deviation threshold, determine the ambient light deviation and calculate the product of the illuminance deviation ratio and the mean color temperature value as the environmental compensation parameter. 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 value of the current lighting device and the dynamic target color temperature. If the color temperature difference is greater than the grading threshold, a staged adjustment mode is adopted. The number of adjustment stages is divided according to the ratio of the color temperature difference. The single adjustment range of each stage is set 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. According to the total amplitude and time axis of the color temperature adjustment sequence, the color temperature adjustment sequence is optimized and smoothed to generate a color temperature gradient adjustment curve.

6. The method for controlling a lighting device with adjustable color temperature according to claim 1, wherein: The specific steps of obtaining the initial target color temperature based on the user control instruction include: Parse the user control command to determine whether there is a forced color temperature setting. If so, use the forced color temperature value as the initial target color temperature; Configure the warning threshold, retrieve the standard color temperature value of the corresponding time period from the time scene color temperature benchmark library according to 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 there is no forced color temperature setting, determine whether to set a scene mode. 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 the scene mode is not set, the standard color temperature value of the corresponding time period is retrieved from the time scene color temperature reference library according to the current timestamp as the initial target color temperature.

7. The method for controlling a lighting device with adjustable color temperature according to claim 2, wherein: The specific steps of determining whether the ambient light change is periodic include: Configure the autocorrelation window and intercept the color temperature value subsequence and illuminance value subsequence within the autocorrelation window before the current timestamp; A time delay sequence is set, and for the color temperature value subsequence and the illuminance value subsequence, an autocorrelation coefficient is calculated at each time delay point in the time delay sequence by using an 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.

8. The method for controlling a lighting device with adjustable color temperature according to claim 1, wherein: The specific steps of dynamically calculating and correcting the deviation of the driving parameters of the lighting device and adjusting the color temperature include: Divide the output brightness of the lighting device into intervals, determine the interval to which the output brightness of the current lighting device belongs, including a low brightness interval, a medium brightness interval, and a high brightness interval, and call a nonlinear light mixing model for the corresponding interval; The nonlinear light mixing model is used to establish a nonlinear mapping relationship between an input vector and the PWM duty cycle of the cold and warm light channels. The elements of the input vector include: target color temperature value, brightness range, LED junction temperature value, and drive current deviation; Collect LED junction temperature data, obtain the driving current feedback signal of the cold and warm light channels, and construct the input vector based on the target color temperature value of the color temperature gradient adjustment curve in combination with the preset spectral ratio parameters; The nonlinear light mixing model is used to perform forward propagation calculations to generate the basic PWM duty cycles of the cold and warm light channels.

9. The method for controlling a lighting device with adjustable color temperature according to claim 8, wherein: The specific steps of dynamically calculating and correcting the deviation of the driving parameters of the lighting device and adjusting the color temperature further include: If the output brightness of the current lighting device belongs to the low brightness range, a correction coefficient for the cold light channel duty cycle is generated to compensate for the color temperature offset 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 cold and warm 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 basic PWM duty cycle. An aging model is pre-established based on LED light decay characteristics. Combined with the accumulated operating time and historical light decay data of the lighting device, the current light decay level is calculated and the corresponding luminous flux compensation coefficient is obtained. The update cycle is configured and the luminous flux compensation coefficient is regularly updated to adjust the basic PWM duty cycle. The actual output color temperature of the lighting device is monitored in real time, compared with the target color temperature value, and 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, the secondary correction mechanism is triggered to correct the PWM duty cycle.

10. A lighting device with adjustable color temperature, controlled by the control method of a lighting device with adjustable color temperature according to any one of claims 1 to 9, characterized in that: Including perception interaction module, execution decision module and intelligent control module: The perception and interaction module is used to collect ambient light data and receive user control instructions; the execution and decision-making module is used to perform ambient light complexity classification based on the ambient light data, determine the initial target color temperature in combination with the user control instructions, and generate a color temperature gradient adjustment curve; the intelligent control module is used to dynamically calculate and correct deviations of the driving parameters of the lighting device according to the color temperature gradient adjustment curve to perform color temperature control.

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