A multi-channel adjustment system for an LED lamp

Abstract

The present invention relates to the technical field of LED lamp regulation, and particularly to a multi-channel regulation system for LED lamps. The method includes the following steps: The parameter acquisition module obtains multi-channel real-time light intensity parameters, performs noise covariance estimation and channel interference analysis, and generates a noise matrix and a channel crosstalk matrix. The change simulation module predicts light intensity changes based on these matrices and generates a simulated LED lamp group. The dead zone compensation module calculates the duty cycle and performs compensation to form an anti-overlap sequence. In the phase allocation module, the anti-overlap sequence is subjected to phase quantization and staggered peak allocation to obtain a staggered peak control sequence. The channel coordination module generates a multi-channel control sequence according to temperature compensation processing. The feedback optimization module continuously adjusts the LED lamp group by collecting and feedbacking strong light values to achieve precise multi-channel regulation. The present invention improves the accuracy of LED lamp regulation.

Description

Technical Field

[0001] The present invention relates to the technical field of LED lamp regulation, and particularly to a multi-channel regulation system for LED lamps. Background Art

[0002] In modern lighting technology, LED lamps are widely used due to their high efficiency and long lifespan. In actual use, LED lamp groups often face problems such as uneven light intensity, insufficient regulation accuracy, and excessive energy consumption. These problems not only affect the lighting effect but may also lead to energy waste and environmental burden. Traditional light intensity regulation methods often rely on simple manual control or basic automation algorithms, resulting in a slow response speed to environmental changes and difficulty in achieving real-time regulation. Interference between light sources and signal noise also affect the accuracy of regulation, thereby causing a decline in the overall lighting effect. The deficiencies of existing technologies in noise management and channel interference analysis limit the reliability of light intensity prediction and regulation. Summary of the Invention

[0003] Based on this, it is necessary to provide a multi-channel regulation system for LED lamps to solve at least one of the above technical problems.

[0004] To achieve the above objective, a multi-channel regulation system for LED lamps includes:

[0005] A parameter acquisition module, configured to obtain multi-channel real-time light intensity parameters; perform noise covariance estimation on the multi-channel real-time light intensity parameters to obtain a noise matrix; perform channel interference analysis on the multi-channel real-time light intensity parameters to generate a channel crosstalk matrix;

[0006] A change simulation module, configured to predict light intensity changes of the real-time light intensity parameters based on the noise matrix and the channel crosstalk matrix to generate predicted light intensity values; perform change simulation processing according to the predicted light intensity values to obtain a simulated LED lamp group;

[0007] A dead zone compensation module, configured to calculate the multi-channel duty ratios of the simulated LED lamp group to obtain a basic duty sequence; perform dead zone compensation processing on the basic duty sequence to generate an anti-overlap sequence;

[0008] A phase allocation module, configured to perform phase quantization on the anti-overlap sequence to obtain the number of phases; perform phase staggering allocation on the anti-overlap sequence based on the number of phases to obtain a staggered control sequence;

[0009] A channel coordination module, configured to perform multi-channel temperature compensation on the staggered control sequence to obtain a temperature-compensated sequence; perform multi-channel coordination processing according to the temperature-compensated sequence to generate a multi-channel control sequence;

[0010] A feedback optimization module is used to perform multi-channel adjustment control according to multi-channel control sequences, and simultaneously collect feedback strong light values; perform feedback optimization on the simulated LED lamp group based on the feedback strong light values to execute multi-channel adjustment of the LED lamp.

[0011] Through the present invention, the real-time acquisition ability of light intensity parameters is improved, enabling the system to quickly respond to environmental changes, enhancing the flexibility and adaptability of adjustment. The combination of noise covariance estimation and channel interference analysis improves the accurate recognition ability of signal noise, ensuring the reliability and stability of data. Through light intensity change prediction, prospective adjustment of light intensity can be achieved, optimizing the performance of the LED lamp group. Dead zone compensation processing effectively eliminates the signal overlap problem, improving the accuracy and efficiency of multi-channel control. The application of phase staggering allocation technology optimizes the light emission timing of the light source, reducing light efficiency loss and energy consumption. Multi-channel temperature compensation ensures light intensity consistency under different environmental conditions, maintaining the stability of the lighting effect. The feedback optimization mechanism continuously adjusts system parameters by real-time feedback of strong light values, improving the accuracy and intelligent level of adjustment. The overall system structure is reasonably designed, and each module cooperates smoothly, enhancing the integration and maintainability of the system.

[0012] Preferably, the parameter acquisition module is used to: obtain multi-channel real-time light intensity parameters; perform noise covariance estimation on the multi-channel real-time light intensity parameters to obtain a noise matrix; perform channel interference analysis on the multi-channel real-time light intensity parameters to generate a channel crosstalk matrix, specifically used for:

[0013] Obtain multi-channel real-time parameters; perform smoothing filtering processing on the multi-channel real-time parameters to obtain filtered light intensity parameters;

[0014] Perform state space mapping on the filtered light intensity parameters to obtain a light intensity state matrix; perform noise covariance estimation on the light intensity state matrix to obtain a noise matrix;

[0015] Perform multi-channel topology construction on the multi-channel real-time light intensity parameters to obtain a network interference topology structure;

[0016] Perform channel crosstalk analysis on the multi-channel real-time light intensity parameters based on the network interference topology structure to generate a channel crosstalk matrix.

[0017] The present invention improves the response speed and reliability of the system by accurately obtaining multi-channel real-time light intensity parameters. The smoothing filter processing effectively reduces the influence of noise on the light intensity parameters, ensuring the stability and accuracy of the data. The state space mapping optimizes the representation of the light intensity data, enhancing the effectiveness of subsequent analysis. The noise covariance estimation provides noise characteristic information, supporting more accurate signal processing. The multi-channel topology construction improves the analysis ability of network interference, ensuring the effective identification of interference relationships between different channels. The channel crosstalk matrix provides an important basis for subsequent light intensity adjustment, helping to achieve a more accurate control strategy.

[0018] Preferably, the change simulation module is used to: predict the light intensity change of the real-time light intensity parameters based on the noise matrix and the channel crosstalk matrix to generate a predicted light intensity value; perform change simulation processing according to the predicted light intensity value to obtain a simulated LED lamp group, specifically used for:

[0019] Perform channel correlation mapping on the noise matrix based on the channel crosstalk matrix to obtain a crosstalk correlation matrix;

[0020] Perform spectral conversion on the real-time light intensity parameters based on the crosstalk correlation matrix to generate a light intensity spectrum matrix;

[0021] Perform light intensity change prediction based on the light intensity spectrum matrix to obtain a predicted light intensity value; perform light intensity parameter mapping on the predicted light intensity value according to the preset basic parameters of the lamp group to generate corresponding light intensity parameters;

[0022] Perform lamp group topology reconstruction on the corresponding light intensity parameters to obtain a simulated LED lamp group.

[0023] The present invention improves the response ability and adaptability of the LED lamp group through the light intensity change prediction of the real-time light intensity parameters. The channel correlation mapping effectively reveals the relationship between noise and crosstalk, enhancing the interference management ability of the system. The generation of the light intensity spectrum matrix makes the light intensity change analysis more comprehensive and accurate. The light intensity parameter mapping provides an optimized configuration for the lamp group based on real-time data. The lamp group topology reconstruction ensures the performance and stability of the simulated LED lamp group, improving the accuracy and efficiency of light intensity adjustment as a whole and providing a solid foundation for intelligent lighting applications.

[0024] Preferably, the dead zone compensation module is used to: calculate the multi-channel duty cycle of the simulated LED lamp group to obtain a basic duty cycle sequence; perform dead zone compensation processing on the basic duty cycle sequence to generate an anti-overlap sequence, specifically used for:

[0025] Perform channel brightness equalization processing on the simulated LED lamp group to obtain the balanced light intensity of the lamp group;

[0026] Perform multi-channel gamma mapping on the balanced light intensity of the lamp group to generate an equalized linear matrix;

[0027] Perform channel interaction compensation deduction on the simulated LED lamp group based on an equilibrium linear matrix to obtain an interaction compensation matrix;

[0028] Perform multi-dimensional duty cycle calculation on the interaction compensation matrix to generate a basic duty cycle sequence;

[0029] Perform edge conflict detection on the basic duty cycle sequence to obtain timing scheduling parameters; perform dead zone balance reconstruction based on the timing scheduling parameters to generate an anti-overlap sequence.

[0030] The present invention ensures the accuracy of the basic duty cycle sequence of the simulated LED lamp group through multi-channel duty cycle calculation. The dead zone compensation process effectively avoids the phenomenon of light overlap, improves the brightness uniformity of the lamp group, the channel brightness balance process enhances the overall light intensity performance of the lamp group, the generation of the equilibrium linear matrix makes the light intensity distribution more uniform, the deduction of the interaction compensation matrix provides effective compensation for the mutual influence between different channels, the edge conflict detection ensures the rationality and effectiveness of the timing scheduling, and the dead zone balance reconstruction further optimizes the operation efficiency of the lamp group, overall improving the adjustment performance and lighting quality of the LED lamp.

[0031] Preferably, the specific steps of performing edge conflict detection on the basic duty cycle sequence to obtain timing scheduling parameters; performing dead zone balance reconstruction based on the timing scheduling parameters to generate an anti-overlap sequence are as follows:

[0032] Perform multi-channel edge recognition on the basic duty cycle sequence to obtain multi-channel switching boundaries; perform switching conflict detection based on the multi-channel switching boundaries to generate switching conflict points;

[0033] Assign priorities to the switching conflict points to obtain timing scheduling parameters;

[0034] Perform scheduling simulation on the basic duty cycle sequence based on the timing scheduling parameters to obtain a simulated scheduling behavior;

[0035] Perform dead zone demand analysis on the simulated scheduling behavior to generate a set of adjustment intervals;

[0036] Perform inter-channel balance compensation on the basic duty cycle sequence according to the set of adjustment intervals to obtain an anti-overlap sequence.

[0037] The present invention ensures the accurate positioning of the switching boundaries through multi-channel edge recognition, improves the efficiency of conflict detection. The generation of the switching conflict points provides key data for subsequent scheduling. The priority assignment optimizes the timing scheduling parameters, enhancing the flexibility and effectiveness of the scheduling. The scheduling simulation provides an intuitive reference for the actual behavior. The dead zone demand analysis ensures the reasonable setting of the adjustment intervals. The inter-channel balance compensation achieves the uniformity of the light intensity output. The generation of the anti-overlap sequence effectively avoids the phenomenon of light overlap, overall improving the performance and adjustment accuracy of the LED lamp group.

[0038] Preferably, the phase allocation module is configured to: perform phase quantization on the anti-overlap sequence to obtain the number of phases; perform phase staggering allocation on the anti-overlap sequence based on the number of phases to obtain a staggering control sequence, specifically:

[0039] Perform phase quantization processing on the anti-overlap sequence to obtain a quantized phase sequence;

[0040] Perform phase frequency statistics on the phase quantization sequence to generate the number of phases;

[0041] Perform power grouping analysis on the anti-overlap sequence based on the number of phases to obtain a phase configuration reference system;

[0042] Perform interference optimization rearrangement on the anti-overlap sequence according to the phase configuration reference system to generate a staggering control sequence.

[0043] The present invention ensures that the phase information of the anti-overlap sequence is accurately captured through phase quantization processing. The quantized phase sequence provides basic data for subsequent analysis. The phase frequency statistics reveals the phase distribution characteristics. The generated number of phases provides a basis for power grouping analysis. The power grouping analysis effectively optimizes the phase configuration reference system. The interference optimization rearrangement improves the light intensity stability of the anti-overlap sequence. The generation of the staggering control sequence reduces the interference during lamp switching, improves the operation efficiency and illumination quality of the LED lamp group, and overall enhances the accuracy of lamp regulation.

[0044] Preferably, the specific steps of performing interference optimization rearrangement on the anti-overlap sequence according to the phase configuration reference system to generate a staggering control sequence are as follows:

[0045] Perform channel distribution identification on the anti-overlap sequence according to the phase configuration reference system to obtain a channel distribution sequence;

[0046] Perform phase aggregation region segmentation according to the channel distribution sequence to obtain phase aggregation coordinates;

[0047] Perform channel grouping based on the phase aggregation coordinates to generate a channel grouping set;

[0048] Perform inter-group interference evaluation on the channel grouping set to generate an inter-channel interference value;

[0049] Perform rearrangement target screening on the channel grouping set based on the inter-channel interference value to generate a rearrangement target channel;

[0050] Perform sequence rearrangement processing on the rearrangement target channel according to the phase configuration reference system to generate a staggering control sequence.

[0051] The present invention ensures clearer channel management for anti-overlap sequences through channel distribution recognition. The generation of the channel distribution sequence provides data support for the subsequent segmentation of the phase aggregation region. The determination of the phase aggregation coordinates accurately reflects the mutual relationship between channels. The formation of channel grouping optimizes the coordination among groups. The inter-group interference evaluation provides a basis for calculating the interference value between channels, effectively identifying potential interference problems. The screening of the rearranged target channels improves the flexibility and response speed of the system. The sequence rearrangement process ensures the continuity and stability of the light intensity output based on the phase configuration reference system. The generation of the peak-shifting control sequence effectively avoids the phenomena of light overlap and flicker, overall improving the adjustment accuracy and usage experience of the LED lamp group, and providing guarantee for the reliability of the intelligent lighting system.

[0052] Preferably, the channel coordination module is used for: performing multi-channel temperature compensation on the peak-shifting control sequence to obtain a temperature-compensated sequence; performing multi-channel coordination processing according to the temperature-compensated sequence to generate a multi-channel control sequence, specifically used for:

[0053] Performing a thermal field gradient mapping on the peak-shifting control sequence based on preset basic parameters of the lamp group to obtain a temperature influence parameter;

[0054] Performing channel temperature coefficient compensation on the peak-shifting control sequence according to the temperature influence parameter to obtain the temperature-compensated sequence of each channel;

[0055] Performing compensation integration processing on the temperature-compensated sequences of each channel to generate a temperature-compensated sequence;

[0056] Performing channel coordination processing according to the temperature-compensated sequence to obtain a channel correction coefficient; performing light flux reconstruction based on the channel correction coefficient to generate a light efficiency compensation sequence;

[0057] Performing time-domain synchronization processing on the light efficiency compensation sequence to generate a multi-channel control sequence.

[0058] The present invention ensures the stability and reliability of the peak-shifting control sequence through multi-channel temperature compensation. The thermal field gradient mapping accurately reflects the influence of temperature on the performance of the lamp group, providing a scientific basis for the temperature influence parameter. The channel temperature coefficient compensation improves the light intensity consistency of each channel. The generation of the temperature-compensated sequence effectively integrates the temperature characteristics of each channel. The channel coordination processing lays a foundation for the overall performance optimization of the system. The calculation of the channel correction coefficient makes the light flux reconstruction more accurate. The generation of the light efficiency compensation sequence improves the energy efficiency performance of the light source. The time-domain synchronization processing ensures the coordination and consistency of the control sequence, overall improving the lighting effect and usage efficiency of the LED lamp group in different environments, and providing strong support for the stable operation of the intelligent lighting system.

[0059] Preferably, the feedback optimization module is configured to: perform multi-channel adjustment control according to a multi-channel control sequence, and simultaneously collect feedback strong light values; perform feedback optimization on the simulated LED lamp group based on the feedback strong light values to perform multi-channel adjustment of the LED lamp, specifically:

[0060] Perform hardware timing conversion on the multi-channel control sequence to obtain a drive control signal;

[0061] Perform multi-channel adjustment control according to the drive control signal, and simultaneously collect feedback strong light values;

[0062] Perform light intensity deviation comparison on the simulated LED lamp group according to the feedback strong light values to obtain a light intensity deviation comparison value;

[0063] Perform feedback optimization on the simulated LED lamp group based on the light intensity deviation comparison value to perform multi-channel adjustment of the LED lamp.

[0064] The present invention ensures the response speed and accuracy of the LED lamp group through multi-channel adjustment control. The hardware timing conversion effectively converts the multi-channel control sequence into a drive signal, improving the stability of the system. The collection of feedback strong light values provides basic data for real-time adjustment. The light intensity deviation comparison realizes the accurate comparison of the actual light intensity and the preset value. The calculation of the light intensity deviation comparison value provides a basis for subsequent optimization. The feedback optimization mechanism improves the adjustment accuracy and flexibility of the LED lamp group. Performing multi-channel adjustment ensures the consistency of the lighting effect in different environments. Overall, it enhances the performance and user experience of the LED lamp group, providing strong support for the adaptive adjustment of the intelligent lighting system. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] Figure 1 It is a schematic structural diagram of a multi-channel adjustment system for an LED lamp. DETAILED DESCRIPTION OF THE INVENTION

[0066] The technical method of the present invention will be clearly and completely described below with reference to the accompanying drawings.

[0067] To achieve the above object, please refer to Figure 1 , a multi-channel adjustment system for an LED lamp includes:

[0068] A parameter acquisition module, configured to obtain multi-channel real-time light intensity parameters; perform noise covariance estimation on the multi-channel real-time light intensity parameters to obtain a noise matrix; perform channel interference analysis on the multi-channel real-time light intensity parameters to generate a channel crosstalk matrix;

[0069] A change simulation module, configured to perform light intensity change prediction on the real-time light intensity parameters based on the noise matrix and the channel crosstalk matrix to generate a predicted light intensity value; perform change simulation processing according to the predicted light intensity value to obtain a simulated LED lamp group;

[0070] The dead zone compensation module is used to calculate the duty ratios of multiple paths for the simulated LED lamp group to obtain a basic duty sequence; perform dead zone compensation processing on the basic duty sequence to generate an anti-overlap sequence;

[0071] The phase allocation module is used to perform phase quantization on the anti-overlap sequence to obtain the number of phases; perform phase staggering allocation on the anti-overlap sequence based on the number of phases to obtain a staggering control sequence;

[0072] The channel coordination module is used to perform multi-channel temperature compensation on the staggering control sequence to obtain a temperature-compensated sequence; perform multi-channel coordination processing according to the temperature-compensated sequence to generate a multi-channel control sequence;

[0073] The feedback optimization module is used to perform multi-channel adjustment control according to the multi-channel control sequence, and at the same time collect the feedback strong light value; perform feedback optimization on the simulated LED lamp group based on the feedback strong light value to perform multi-channel adjustment of the LED lamp.

[0074] The present invention improves the real-time acquisition ability of light intensity parameters, enables the system to quickly respond to environmental changes, enhances the flexibility and adaptability of adjustment, combines noise covariance estimation and channel interference analysis, improves the accurate recognition ability of signal noise, ensures the reliability and stability of data, can realize the forward-looking adjustment of light intensity through light intensity change prediction, optimizes the performance of the LED lamp group, the dead zone compensation processing effectively eliminates the signal overlap problem, improves the accuracy and efficiency of multi-channel control, the application of the phase staggering allocation technology optimizes the light emission timing of the light source, reduces light efficiency loss and energy consumption, the multi-channel temperature compensation ensures the light intensity consistency under different environmental conditions, maintains the stability of the lighting effect, the feedback optimization mechanism continuously adjusts the system parameters by real-time feedback of the strong light value, improves the accuracy and intelligent level of adjustment, the overall system structure is reasonably designed, each module cooperates smoothly, and enhances the integration and maintainability of the system.

[0075] In the embodiment of the present invention, refer to Figure 1 , which is a schematic structural diagram of a multi-channel adjustment system for an LED lamp according to the present invention. In this instance, the multi-channel adjustment system for an LED lamp includes:

[0076] The parameter acquisition module is used to obtain multi-channel real-time light intensity parameters; perform noise covariance estimation on the multi-channel real-time light intensity parameters to obtain a noise matrix; perform channel interference analysis on the multi-channel real-time light intensity parameters to generate a channel crosstalk matrix;

[0077] In this embodiment, the optical intensity signals output by the LED lights are collected in real time through a multi-channel optical sensor array. The model of the optical sensor is the TSL2591 high-precision digital optical intensity sensor, and its data interface is the I2C protocol (two-wire interface). The sampling frequency is set to 500 Hz. Each optical intensity signal is converted into a digital signal through the ADC module (analog-to-digital converter) built into the sensor. After the collection is completed, the optical intensity parameters are transmitted to the central processing unit (CPU) for storage through the STM32F103C8T6 microcontroller. Using the embedded data processing unit, the optical intensity parameters are processed through the real-time covariance calculation method to estimate the noise distribution characteristics. The dimension of the covariance matrix is determined according to the number of acquisition channels. Taking 8 channels as an example, the covariance matrix is an 8×8 symmetric matrix. Subsequently, based on the wavelet decomposition method, the interference characteristics of each optical intensity are extracted, and the crosstalk coefficient between channels is calculated through channel cross-correlation analysis to generate a channel crosstalk matrix. The crosstalk matrix is also an 8×8 symmetric matrix, and the result is saved in the on-chip memory.

[0078] A change simulation module, which is used to predict the optical intensity change of the real-time optical intensity parameters based on the noise matrix and the channel crosstalk matrix to generate a predicted optical intensity value; and perform change simulation processing according to the predicted optical intensity value to obtain a simulated LED lamp group;

[0079] In this embodiment, the noise matrix and the channel crosstalk matrix are called from the parameter acquisition module. With the prediction of optical intensity change as the goal, using the optical intensity change prediction model based on Support Vector Regression (SVR), the input parameters are set as the real-time values of the optical intensity of each channel and the corresponding noise matrix and crosstalk matrix. The prediction model is implemented through the SVR toolbox in MATLAB. The output of the model is the predicted optical intensity value of the next time step of each channel. After the prediction is completed, the predicted optical intensity value is input into the simulation tool. Based on the SPICE (Simulation Program with Integrated Circuit Emphasis) platform, the change simulation of the LED lamp group is carried out to determine the dynamic behavior of each channel under different optical intensity states, and finally a virtual simulation LED lamp group model is generated.

[0080] A dead zone compensation module, which is used to calculate the duty cycle of multiple paths for the simulated LED lamp group to obtain a basic duty cycle sequence; and perform dead zone compensation processing on the basic duty cycle sequence to generate an anti-overlap sequence;

[0081] In this embodiment, the output characteristic data of the simulated LED lamp group is obtained from the simulation module. Based on the dimming scheme of PWM (pulse width modulation), the duty cycle sequence of each channel is calculated. The duty cycle sequence is discretized based on the control period with a minimum time interval of 1 ms. According to the duty cycle sequences of each channel, dead-time compensation for the time interval between adjacent channels is performed. The dead-time compensation is achieved by inserting a blank time period with a minimum pulse width of 0.5 ms. The compensated PWM signal is output using the GPIO (general-purpose input / output) module of STM32, and the result is stored as an anti-overlap sequence.

[0082] The phase allocation module is used to perform phase quantization on the anti-overlap sequence to obtain the number of phases; based on the number of phases, the anti-overlap sequence is subjected to phase staggering allocation to obtain a staggered control sequence.

[0083] In this embodiment, the anti-overlap sequence is called from the dead-time compensation module, and the frequency distribution characteristics of the anti-overlap sequence are analyzed using a phase quantization method based on FFT (fast Fourier transform). The phase quantization accuracy is set to a 1-degree step of 360 degrees, and the number of phases is calculated. According to the number of phases, the anti-overlap sequence is subjected to phase staggering allocation at a fixed interval. The allocation algorithm sets the initial phase offset to 10 degrees and increments by 10 degrees each time until the allocation is completed. The allocated staggered control sequence is written into the memory through the DMA (direct memory access) module of STM32.

[0084] The channel coordination module is used to perform multi-channel temperature compensation on the staggered control sequence to obtain a temperature-compensated sequence; according to the temperature-compensated sequence, multi-channel coordination processing is performed to generate a multi-channel control sequence.

[0085] In this embodiment, the staggered control sequence generated by the phase allocation module is called, and data compensation processing based on a temperature sensor is performed on each channel. The DS18B20 single-bus digital temperature sensor is selected as the temperature sensor, and its accuracy is 0.1 °C. Each sensor collects temperature data in real time and transmits it to the central processing unit through the serial port. According to the collected temperature data, the compensation factor of temperature for light intensity is calculated using the linear interpolation method and superimposed on the staggered control sequence to complete the generation of the temperature-compensated sequence. Subsequently, through a multi-channel coordination method based on an improved weighted average algorithm, the output power of multiple channels is synchronously adjusted to generate the final multi-channel control sequence.

[0086] The feedback optimization module is used to perform multi-channel adjustment control according to the multi-channel control sequence, and at the same time collect the feedback strong light value; based on the feedback strong light value, the simulated LED lamp group is feedback optimized to perform multi-channel adjustment of the LED lamp.

[0087] In this embodiment, multiple control sequences are obtained from the channel coordination module, and the actual LED lamp group is adjusted and controlled through a driving circuit. The driving circuit adopts a constant current source driving scheme, and the output power is 350 mA for each channel. During the adjustment and control process, the feedback light intensity value is collected in real time through a high-sensitivity light intensity sensor. The sensor model is OPT3001, and the resolution is 0.01 lx. After the feedback light intensity value is transmitted to the central processing unit, the feedback light intensity value and the target light intensity value are compared in real time through a PID (Proportional Integral Derivative) control algorithm, the error is calculated, and the control sequence is dynamically adjusted to optimize the multi-channel adjustment performance of the LED lamp, and finally the precise adjustment of the LED lamp group is realized.

[0088] In this embodiment, the parameter acquisition module is used to: obtain multiple real-time light intensity parameters; perform noise covariance estimation on the multiple real-time light intensity parameters to obtain a noise matrix; perform channel interference analysis on the multiple real-time light intensity parameters to generate a channel crosstalk matrix, specifically used for:

[0089] Obtain multiple real-time light parameters; perform smoothing filtering processing on the multiple real-time light parameters to obtain filtered light intensity parameters;

[0090] Perform state space mapping on the filtered light intensity parameters to obtain a light intensity state matrix; perform noise covariance estimation on the light intensity state matrix to obtain a noise matrix;

[0091] Perform multi-channel topology construction on the multiple real-time light intensity parameters to obtain a network interference topology structure;

[0092] Perform channel crosstalk analysis on the multiple real-time light intensity parameters based on the network interference topology structure to generate a channel crosstalk matrix.

[0093] In this embodiment, a TSL2591 light intensity sensor array is adopted. Each sensor is independently connected to the I2C bus of the STM32F429 microcontroller. The sampling frequency is set to 1000 Hz. During the sampling process, each sensor converts the analog light intensity signal into a digital signal through its internal analog-to-digital converter and transmits it to the central processing unit (CPU) with a 16-bit resolution. The collected digital light intensity data is stored in an external FRAM (ferroelectric memory) module for subsequent processing. In the actual layout, 8 light intensity sensors are respectively installed at the light output positions of different channels. The real-time light intensity data stored in the FRAM is subjected to smoothing filtering. The smoothing processing method based on the moving average filtering algorithm is used. The continuous five sampling values of each light intensity parameter are used as a sliding window, and the average value of the data within the sliding window is calculated to smooth out the high-frequency noise. The smoothed data is stored in the internal RAM of the STM32 to reduce the storage and reading delay. The width parameter of the sliding window is determined to be 5 sampling points according to experimental tests. The state space transformation method is used to map the filtered light intensity parameters. The MATLAB Simulink tool is used to construct a state space mapping model. The input parameter is the filtered light intensity data. The state space transformation is based on the system dynamic characteristics of the light intensity channel. Each light intensity signal is transformed from the time domain to the state space domain, and the output is the light intensity state matrix. The dimension of the light intensity state matrix is determined according to the number of light intensity sensors. In the case of 8 channels, the state matrix is an 8×2 two-dimensional matrix, where the first column represents the current state of the light intensity signal, and the second column represents the state change rate of the light intensity signal. After the mapping is completed, the light intensity state matrix is transmitted to the data analysis module. Based on the light intensity state matrix, the noise characteristics of each channel signal are estimated by covariance. The noise covariance calculation method based on the recursive least squares method is adopted. During the covariance calculation process, the covariance matrix is first initialized as a zero matrix, and then each row in the light intensity state matrix is used as an input for dynamic iterative calculation. The calculation result is an 8×8 symmetric covariance matrix. The diagonal elements of the covariance matrix represent the noise variance of each light intensity signal, and the non-diagonal elements represent the noise covariance between channels. Taking the light intensity channels as nodes and the interaction relationship of the light intensity parameters as edges, a network is constructed. The Pearson correlation coefficient-based method is used to quantify the interaction intensity between channels, and the threshold is set to 0.A correlation of 5 or above is used as a valid edge to generate a weighted undirected graph. The node weights in the topological structure represent the intensity of the optical intensity signal, and the edge weights represent the interaction intensity between channels, thereby generating a topological structure. Using the generated multi-channel topological structure, the crosstalk situation between channels is analyzed. The shortest path analysis method based on graph theory is used to calculate the crosstalk path length between channels. At the same time, combined with the interaction weight data of multiple channels, the General Interference Index is used to evaluate the crosstalk intensity of each channel. The result of the crosstalk analysis is a channel crosstalk matrix, and the channel crosstalk matrix is an 8×8 symmetric matrix, where each element represents the interference degree between two channels.

[0094] In this embodiment, the change simulation module is used to: predict the optical intensity change of the real-time optical intensity parameter based on the noise matrix and the channel crosstalk matrix to generate a predicted optical intensity value; perform change simulation processing according to the predicted optical intensity value to obtain a simulated LED lamp group, specifically used for:

[0095] Perform channel correlation mapping on the noise matrix based on the channel crosstalk matrix to obtain a crosstalk correlation matrix;

[0096] Perform spectral conversion on the real-time optical intensity parameter based on the crosstalk correlation matrix to generate an optical intensity spectrum matrix;

[0097] Perform optical intensity change prediction based on the optical intensity spectrum matrix to obtain a predicted optical intensity value; perform optical intensity parameter mapping on the predicted optical intensity value according to the preset basic parameters of the lamp group to generate corresponding optical intensity parameters;

[0098] Perform lamp group topology reconstruction on the corresponding optical intensity parameters to obtain a simulated LED lamp group.

[0099] In this embodiment, the MATLAB tool is used to perform channel correlation mapping processing on the channel crosstalk matrix and the noise matrix. The channel crosstalk matrix (8×8 dimension) is used as the input matrix A, and the noise matrix (8×8 dimension) is used as the input matrix B. Matrix multiplication operation is performed to obtain the channel correlation matrix C. During the mapping process, each row and column of A and B are matched and calculated in sequence. Each element value of the channel correlation matrix C represents the coupling degree of channel noise and crosstalk. The channel correlation matrix C is loaded and corresponding to the sampling sequence of real-time light intensity parameters channel by channel. The sampling sequence is provided by the previously stored filtered light intensity data. After the real-time light intensity parameters are combined with the correlation matrix C, weight distribution calculation is performed to generate a spectral sequence. The spectral sequence data is reorganized into an 8×16 light intensity spectrum matrix, where 8 represents the number of light intensity channels, and 16 represents the spectral segment division per channel. The spectral division range is from 380 nanometers to 780 nanometers (the visible light range of the human eye). The long short-term memory network (LSTM) deep learning model is used to predict the light intensity spectrum matrix. The LSTM model is built in the TensorFlow framework. The input of the model is the time series of the light intensity spectrum matrix, and the output is the future light intensity value per channel. The time window for light intensity change prediction is set to 100 ms. The training data of the model is extracted from the actually sampled light intensity historical data. During the prediction process, the GPU (graphics processing unit) is used to accelerate the calculation to improve the real-time performance of the model. Based on the basic parameters of the lamp group, parameter mapping is performed on the predicted light intensity values. The basic parameters of the lamp group include the power of the LED chip (0.5 W), drive current (20 mA), luminous efficiency (100 lumens per watt), and radiation angle (120 degrees). The predicted light intensity values are converted into actual LED luminous flux values by looking up a table and combined with the geometric position parameters of the lamp group to generate the specific light intensity corresponding parameters for each LED lamp. The light intensity corresponding parameters are recorded in the form of a triple, including the channel number, light intensity value, and spatial coordinates. Based on the light intensity corresponding parameters, the topological structure of the simulated LED lamp group is constructed, and the topological reconstruction is completed using the Blender 3D modeling tool. First, the light intensity corresponding parameters are imported into Blender, and 3D coordinate points are automatically generated through a Python script, and the light intensity value is assigned to each coordinate point as the material luminous intensity. Subsequently, each coordinate point is connected into an overall structure according to the preset physical layout rules of the lamp group.

[0100] In this embodiment, the dead zone compensation module is used to: perform multi-channel duty cycle calculation on the simulated LED lamp group to obtain a basic duty cycle sequence; perform dead zone compensation processing on the basic duty cycle sequence to generate an anti-overlap sequence, specifically used for:

[0101] Perform channel brightness equalization processing on the simulated LED lamp group to obtain the balanced light intensity of the lamp group;

[0102] Perform multi-channel gamma mapping on the balanced light intensity of the lamp group to generate an equalized linear matrix;

[0103] Based on the balanced linear matrix, perform channel interaction compensation deduction on the simulated LED light group to obtain the interaction compensation matrix;

[0104] Perform multi-dimensional duty cycle calculation on the interaction compensation matrix to generate the basic duty cycle sequence;

[0105] Perform edge conflict detection on the basic duty cycle sequence to obtain the timing scheduling parameters; based on the timing scheduling parameters, perform dead zone balance reconstruction to generate the anti-overlap sequence.

[0106] In this embodiment, for each channel in the simulated LED light group, let the original light intensity matrix of the lamp beads be , and the target light intensity be . By calculating the gain matrix to achieve brightness balance, the gain calculation formula is as follows:

[0107] ;

[0108] Apply the gain matrix to the original light intensity matrix to obtain the balanced light intensity matrix , and the calculation formula is as follows:

[0109] ;

[0110] Suppose is 120 cd, and the original light intensity of a certain channel is 90 cd, then the gain is 1.33, and the balanced light intensity , which is 120 cd. The gain matrix and the equalization matrix are calculated through matrix operation tools (such as MATLAB or the NumPy library in Python). The matrix dimension is the same as the number of LED channels. The balanced light intensity data is subjected to gamma mapping. During this process, the light intensity of each channel is transformed by a specified gamma coefficient, which is selected by the system according to the preset brightness standard. Assuming the gamma coefficient is 2.2, the light intensity value of each channel will be adjusted according to this coefficient to make the light intensity distribution more uniform. For example, if the balanced light intensity of a certain channel is 80 units, after gamma mapping transformation, the mapping result of this channel is the transformed light intensity value. The mapping values of all channels are calculated and updated in sequence to obtain the light intensity data after gamma mapping. The light intensity data of each channel is collected and recorded as a matrix. Then, the compensation coefficient of each channel is calculated, which is dynamically adjusted according to the interaction effect of adjacent channels. The goal of compensation is to eliminate the mutual interference between channels. Assuming the compensation coefficient of a certain channel is 1.1 and that of another channel is 0.9, during the compensation process, the compensation coefficient of each channel will be adjusted by an algorithm, and finally an interaction compensation matrix is generated, which contains the final compensation coefficients of all channels. The light intensity data of each channel after compensation is updated. The light intensity value of each channel is compared with the target light intensity value to calculate the duty cycle. Based on the light intensity data after compensation and the target light intensity data, a basic duty cycle sequence is generated. For example, assuming the light intensity after compensation of a certain channel is 70 units and the target light intensity is 100 units, the duty cycle of this channel is 70%. After similar calculations for all channels, a basic duty cycle sequence is obtained. Edge conflict detection is performed on the generated basic duty cycle sequence to check the time interval between the rising edge and the falling edge of the duty cycle signal. If the time difference between the rising edge and the falling edge of the two signals is less than the preset dead zone time threshold, it is determined that there is a conflict. Assuming the time difference between the rising edge and the falling edge of the two signals is 2 units and the dead zone time threshold is set to 5 units, it is determined that a conflict has occurred. The conflict information is recorded and fed back to the scheduling module. Based on the timing scheduling parameters, dead zone balance reconstruction is performed to optimize the time interval between signals by adjusting the triggering timing of the signals to avoid the occurrence of conflicts. If the interval between signals is detected to be less than the dead zone time threshold, the time interval is adjusted to meet the preset dead zone time requirement. For example, if the time interval between the two signals is 2 units and the dead zone time threshold is 5 units, the triggering timing of one of the signals is adjusted to make the interval 5 units, thus avoiding overlap. After adjustment, an anti-overlap duty cycle sequence is generated and transmitted to the LED driving module, and finally an optimized signal is output.

[0111] In this embodiment, the specific steps of performing edge conflict detection on the basic duty cycle sequence to obtain timing scheduling parameters and performing dead zone balance reconstruction based on the timing scheduling parameters to generate an anti-overlap sequence are as follows:

[0112] Perform multi-channel edge recognition on the basic duty cycle sequence to obtain multi-channel switching boundaries; perform switching conflict detection based on the multi-channel switching boundaries to generate switching conflict points;

[0113] Assign priorities to the switching conflict points to obtain timing scheduling parameters;

[0114] Perform scheduling simulation on the basic duty cycle sequence based on the timing scheduling parameters to obtain simulated scheduling behaviors;

[0115] Perform dead zone demand analysis on the simulated scheduling behaviors to generate a set of adjustment intervals;

[0116] Perform inter-channel balance compensation on the basic duty cycle sequence according to the set of adjustment intervals to obtain an anti-overlap sequence.

[0117] In this embodiment, by capturing the rising and falling edge positions of signals, the switching point positions of each signal are determined, and the detected switching points are recorded in the storage unit in the form of timestamps. The edge detection module samples each signal at a level of 100 nanoseconds through a high-speed sampler. Suppose the sampling result of a certain signal shows that the rising edge time is 10 microseconds and the falling edge time is 15 microseconds. Then the switching boundaries are the time points of 10 microseconds and 15 microseconds. The same processing is performed on all signals in sequence, and finally a multi-channel switching boundary data table is generated. This data table includes the rising and falling edge time points of each signal. The multi-channel switching boundary data table is analyzed using the switching conflict detection module, and the rising and falling edge time points of each signal are compared. If it is found that the boundary time difference between adjacent signals is less than the minimum switching interval threshold set by the system, it is marked as a switching conflict point. Suppose the minimum switching interval threshold set by the system is 5 microseconds. If the falling edge time of signal A is 20 microseconds and the rising edge time of signal B is 23 microseconds, this situation will not trigger a conflict. However, if the rising edge time of signal B is 24 microseconds, it is determined as a switching conflict, and the conflict point is recorded as 24 microseconds. All conflict points are recorded in the switching conflict point data list. For each conflict point in the switching conflict point data list, it is processed based on the signal priority sorting rule. The priority sorting rule is set according to parameters such as the importance and switching frequency of the signals. The importance is defined by the priority weight matrix set by the system, and the switching frequency is calculated by analyzing the number of signal switches in the basic duty cycle sequence. Suppose the priority weight of signal A is 0.8 and that of signal B is 0.If it is 6, then at 24 microseconds at the conflict point, signal A preferentially obtains scheduling resources. The priority allocation module will generate timing scheduling parameters according to the calculation results, mark signal A as the priority signal, and record the delay time of signal B. The generated timing scheduling parameters include the priority signal and the delay adjustment values of other signals. The timing scheduling module is used to perform simulated scheduling on the basic duty cycle sequence. According to the priority signal and the delay adjustment values in the timing scheduling parameters, the signal switching timing at the conflict point is rearranged. The specific operation is to keep the priority signal's original switching timing unchanged and postpone the switching of other signals according to the delay adjustment values. Assume that signal A is the priority signal and its switching timing remains unchanged, and the rising edge time of signal B is postponed from 24 microseconds to 26 microseconds. The scheduling simulation module runs by loading the adjusted duty cycle timing into the simulation environment, observes the overlapping situation of the signals, and records the simulation results to generate a simulated scheduling behavior log. This log includes the switching timing of the adjusted signals and the conflict detection results. The dead zone requirement analysis module is used to analyze the simulated scheduling behavior log, compare the switching timing of the signals with the minimum dead zone time requirement. If it is found that the time interval of the adjusted signals is still lower than the minimum dead zone time threshold, further adjustment is required. For this purpose, by analyzing the position and frequency of the conflict point, a set of adjustment intervals containing all the time intervals that need to be adjusted is generated. Assume that the minimum dead zone time threshold is 5 microseconds. If the falling edge time of signal A is 30 microseconds and the rising edge time of signal B is 33 microseconds, then this interval is less than the dead zone time, and the interval range from 30 to 33 microseconds is recorded in the set of adjustment intervals. The inter-channel balance compensation module is used to adjust the basic duty cycle sequence according to the interval range in the set of adjustment intervals. By adjusting the signal trigger time and the duty cycle width, ensure that the time intervals of all signals meet the minimum dead zone time requirement. Assume that within the interval from 30 to 33 microseconds, the falling edge time of signal A is adjusted to 29 microseconds and the rising edge time of signal B is adjusted to 34 microseconds to widen the time interval between the signals. The inter-channel balance compensation module processes all the interval ranges that need to be adjusted in sequence, and the adjusted duty cycle sequence is the anti-overlap sequence.

[0118] In this embodiment, the phase allocation module is used to: perform phase quantization on the anti-overlap sequence to obtain the number of phases; perform phase staggering allocation on the anti-overlap sequence based on the number of phases to obtain a staggering control sequence, specifically used for:

[0119] Perform phase quantization processing on the anti-overlap sequence to obtain a quantized phase sequence;

[0120] Perform phase frequency statistics on the phase quantization sequence to generate the number of phases;

[0121] Perform power grouping analysis on the anti-overlap sequence based on the number of phases to obtain a phase configuration reference system;

[0122] Interference optimization rearrangement is performed on the anti-overlap sequence according to the phase configuration reference system to generate a peak staggering control sequence.

[0123] In this embodiment, the switching timing of each signal in the anti-overlap sequence is sampled by the phase quantization module. The phase quantization interval is set to 10 microseconds. The signal switching time is aligned with the phase quantization interval in sequence. The specific operation is to align the switching timing of the signal to the nearest integer multiple according to the phase quantization interval. For example, if the switching timing of a certain signal is 35 microseconds, it is adjusted to 40 microseconds according to the quantization interval. At the same time, the adjusted time point is recorded. The recorded content includes the channel number of the signal and the adjusted switching time point. The quantized data of all signals is recorded as the quantized phase sequence. The quantized phase sequence is stored in tabular form, including the signal channel number, the adjusted rising edge time, the adjusted falling edge time, etc. The signals are grouped according to the integer value of the quantized time point, and then the number of signals corresponding to each time point is counted. Suppose the switching time points of some signals in the quantized phase sequence are 40 microseconds, 50 microseconds, and 60 microseconds. Then these three time points are respectively counted as three phase groups. The number of signals in each phase group is used as the frequency value of this phase. At the same time, the total number of all phase groups is calculated. The frequency statistics module will generate a phase number data table. This table includes the time points of each phase group and the corresponding number of signals. The signals are grouped according to the time points in the phase number data table, and the power values of each group of signals are accumulated and calculated. Suppose a certain phase group includes 3 signals, and the power values of each signal are 1 watt, 2 watts, and 3 watts respectively. Then the total power of this phase group is 6 watts. The power grouping analysis module calculates all phase groups in sequence and records the total power of each phase group as the phase power value. At the same time, the phase power values are sorted. The phase group with the highest total power is marked as the priority phase to generate the phase configuration reference system. The phase configuration reference system includes the time points of each phase group, the total power value, and the priority label. The interference optimization module performs signal rearrangement on the anti-overlap sequence and adjusts the signal switching timing according to the priority label in the phase configuration reference system. The signals in the priority phase are arranged in the time period with the least switching interference. The specific operation is to reassign the switching time points of all signals in the phase group. For example, if the time point of a certain priority phase group is 40 microseconds and the total power value is 6 watts, then the switching time points of the signals in this phase group are adjusted to the adjacent interference-free time period, such as 45 microseconds, while ensuring that the switching interval between signals meets the minimum switching interval requirement. After the adjustment is completed, the interference optimization module generates a peak staggering control sequence. The peak staggering control sequence includes the rearranged signal switching timing and phase group information, and is finally used to drive the LED lamp to achieve stable output.

[0124] In this embodiment, the specific steps of performing interference optimization rearrangement on the anti-overlap sequence according to the phase configuration reference system to generate a peak staggering control sequence are as follows:

[0125] Identify the channel distribution of the anti-overlap sequence with respect to the phase configuration reference system to obtain the channel distribution sequence;

[0126] Segment the phase aggregation region according to the channel distribution sequence to obtain the phase aggregation coordinates;

[0127] Group the channels based on the phase aggregation coordinates to generate a set of channel groups;

[0128] Evaluate the interference between groups of the set of channel groups to generate the inter-channel interference value;

[0129] Screen the rearrangement targets for the set of channel groups based on the inter-channel interference value to generate the rearrangement target channels;

[0130] Perform sequence rearrangement processing on the rearrangement target channels according to the phase configuration reference system to generate a stagger control sequence.

[0131] In this embodiment, the channel identification module analyzes the channel information in the phase configuration reference system, extracts the signal switching time points and power values corresponding to each channel, and records the switching times and power distributions of all channels in tabular form. For example, assume that the switching time points of channel A are 20 microseconds, 30 microseconds, and 40 microseconds, and the powers are 2 watts, 3 watts, and 5 watts respectively. The switching time points of channel B are 25 microseconds, 35 microseconds, and 45 microseconds, and the powers are 3 watts, 4 watts, and 6 watts respectively. Then, the information of each channel is summarized and sorted to form a channel distribution sequence. The region segmentation module performs clustering analysis on the time point data in the channel distribution sequence, segments it using a fixed time interval. For example, set the time interval to 10 microseconds, and count the number of channels and the total power value in each time period. Mark the time period with a channel density exceeding the threshold as a phase aggregation region. Assume that in the time period from 30 to 40 microseconds, it contains channel A and channel B, the total power value is 8 watts, and the channel density of this time period exceeds the preset threshold of 5. Then mark this time period as a phase aggregation region, and record the start and end times simultaneously to form a phase aggregation coordinate table. The phase aggregation coordinates include the start time, end time, and channel density value of all aggregation regions. The grouping module classifies the channels within the phase aggregation region, divides the channels in the same region into a channel group, and records the time range and total power value of each channel group. For example, for the time period from 30 to 40 microseconds in the phase aggregation coordinates, channel A and channel B belong to the signals in this time period. Then divide channel A and channel B into the same channel group, and record the time range of this channel group as 30 to 40 microseconds, and the total power value as 8 watts. After processing all the channel groups in the aggregation regions in sequence, a channel grouping set is generated. The channel grouping set is recorded in list form, and each channel group contains the time range, channel number, and total power value. The interference evaluation module calculates the interference between each pair of channel groups in the channel grouping set. The specific steps are as follows: extract the time ranges of the two groups of signals, calculate the overlapping ratio of the time ranges, and estimate the interference intensity in combination with the total power values of the channel groups. Assume that the time range of channel group 1 is from 30 to 40 microseconds, the total power value is 8 watts, the time range of channel group 2 is from 35 to 45 microseconds, the total power value is 10 watts, and the time overlapping ratio is 50%. Then calculate the interference intensity as 4 watts according to the overlapping ratio and power value. The interference evaluation module records the interference values between all channel groups as an interference matrix, which is stored in the form of a two-dimensional array. Each element in the matrix represents the interference intensity between two channel groups. The screening module sorts the interference values in the interference matrix, extracts the pair of channel groups with the largest interference value, and marks them as the rearrangement target channel groups. Assume that the interference intensity between channel group 1 and channel group 2 in the interference matrix is 4 watts, which is the maximum value among all channel group pairs. Then take channel group 1 and channel group 2 as the rearrangement target channel groups, and further analyze the signal switching time points of the rearrangement target channel groups to extract the signal with the greatest interference impact as the rearrangement target channel.For example, if the switching time point of signal A in channel group 1 is 35 microseconds and the switching time point of signal B in channel group 2 is 36 microseconds, then signals A and B are marked as rearranged target channels, and the time point interval and priority information in the phase configuration reference system are extracted. The signal switching time points of the rearranged target channels are moved to the time period with the lowest priority. For example, according to the priority information of the phase configuration reference system, the priority time period is from 10 to 20 microseconds, and the sub-optimal time period is from 20 to 30 microseconds. Then the switching time of signal A is adjusted to 15 microseconds, and the switching time of signal B is adjusted to 25 microseconds. At the same time, the adjusted time points are verified to ensure that the interval between the adjusted signal switching time points and the switching time points of other signals meets the minimum interval requirement. After the rearrangement module completes the adjustment, a peak staggering control sequence is generated. The peak staggering control sequence records the adjusted signal switching time points and channel numbers in tabular form.

[0132] In this embodiment, the channel coordination module is used to: perform multi-channel temperature compensation on the peak staggering control sequence to obtain a temperature-compensated sequence; perform multi-channel coordination processing according to the temperature-compensated sequence to generate a multi-channel control sequence, specifically used for:

[0133] Perform a thermal field gradient mapping on the peak staggering control sequence based on preset basic parameters of the lamp group to obtain temperature influence parameters;

[0134] Perform channel temperature coefficient compensation on the peak staggering control sequence according to the temperature influence parameters to obtain the temperature-compensated sequence for each channel;

[0135] Perform compensation integration processing on the temperature-compensated sequences of each channel to generate a temperature-compensated sequence;

[0136] Perform channel coordination processing according to the temperature-compensated sequence to obtain a channel correction coefficient; perform optical flux reconstruction based on the channel correction coefficient to generate a light efficiency compensation sequence;

[0137] Perform time-domain synchronization processing on the light efficiency compensation sequence to generate a multi-channel control sequence.

[0138] In this embodiment, the preset basic parameters of the lamp group are analyzed by the thermal field analysis module. The basic parameters such as the current value, power value, and heat generation coefficient of the LED lamp are used as inputs. Combining the time nodes and channel numbers of the peak-shifting control sequence, the thermal field distribution map of the lamp group is generated using the thermal field gradient calculation method. After associating the heat generation with time and position, a thermal field gradient mapping model is established. Assume that the basic parameters of the lamp group include a current of 700 mA, a power of 5 W, and a heat generation coefficient of 0.02 °C / W. Then, for the control signal of each channel, the temperature influence parameter is calculated according to the thermal field distribution. For example, the temperature of the heating area of a certain channel is 45 °C, and the corresponding temperature influence parameter is 2 °C / second. The temperature influence parameters of all channels are stored in matrix form. Each column of the matrix represents the temperature change rate of a channel. The temperature influence parameters are processed channel by channel through the temperature compensation module, and the temperature coefficient compensation value is calculated using the temperature change rate of each channel. The compensation value is superimposed on the corresponding peak-shifting control sequence to generate a temperature compensation sequence. Assume that the time of a signal switching point in the peak-shifting control sequence is 10 μs, the original power value is 2 W, and the corresponding temperature change rate is 2 °C / second. Then the temperature compensation value is calculated as 0.4 W. After adding the compensation value to the original power value, it is adjusted to 2.4 W. The generated temperature compensation sequences of each channel after compensation are stored in a two-dimensional table with time points as rows and channel numbers as columns. Each item represents the power value of a certain channel at a certain time point. Through the compensation integration module, the temperature compensation sequences of all channels are comprehensively analyzed. The temperature compensation value and the sequence of channel power adjustments are merged, and it is ensured that the overall power distribution uniformity is satisfied within the time domain. The specific implementation method is as follows: First, the power values of all channels at each time point are summed up to check whether it exceeds the total power limit of the lamp group. For example, if the sum of the power values of all channels at a certain moment is 15 W and the power limit of the lamp group is 20 W, then no adjustment is required. If the total power exceeds the limit value, the power values of each channel are reduced using the linear weight distribution method to generate the final temperature compensation sequence. The temperature compensation sequence is indexed by channel number and time point. Through the channel coordination module, the temperature compensation sequence is analyzed to extract the power difference between channels at each time point, and the power difference is quantified using the correction coefficient calculation model. Assume that the power of channel A is 3 W and the power of channel B is 2.8 W at a certain time point. Then the power difference is 0.2 W. According to the correction coefficient calculation formula, the difference is converted into a correction coefficient of 0.05. The correction coefficients at all time points are stored in matrix form. Each item of the matrix represents the correction coefficient of a certain channel at the corresponding time point. Multiply the power value of each channel by the correction coefficient to obtain the new power value. Assume that the power of channel A is 3 W and the correction coefficient is 0.05 at a certain time point in the temperature compensation sequence. Then the corrected power value is 3.For 15 W, combine the corrected power values of all channels to form a light efficiency compensation sequence. The light efficiency compensation sequence is indexed by time points and stores the final power values of each channel in matrix form. Precise the signal switching time of each time point to the microsecond level and align the switching times of all channels. Assume that the switching time point of a certain signal in the light efficiency compensation sequence is 10.2 microseconds, and the switching time points of other channels are 10 microseconds and 10.3 microseconds respectively. Then, use the interpolation algorithm to uniformly adjust all switching time points to 10.2 microseconds. After the adjustment is completed, generate a multi-channel control sequence, and the multi-channel control sequence records the final switching time points and the corresponding power values in tabular form.

[0139] In this embodiment, the feedback optimization module is used to: perform multi-channel adjustment control according to the multi-channel control sequence, and simultaneously collect the feedback strong light value; perform feedback optimization on the simulated LED lamp group based on the feedback strong light value to execute the multi-channel adjustment of the LED lamp, specifically used for:

[0140] Perform hardware timing conversion on the multi-channel control sequence to obtain a drive control signal;

[0141] Perform multi-channel adjustment control according to the drive control signal, and simultaneously collect the feedback strong light value;

[0142] Perform light intensity deviation comparison on the simulated LED lamp group according to the feedback strong light value to obtain a light intensity deviation comparison value;

[0143] Perform feedback optimization on the simulated LED lamp group based on the light intensity deviation comparison value to execute the multi-channel adjustment of the LED lamp.

[0144] In this embodiment, the hardware timing conversion module samples each channel signal of the multi-channel control sequence, re-encodes the time nodes and power values of the signals according to the requirements of the hardware driver, and generates a standardized drive signal. The specific operations include reading the signal timing of each channel in the multi-channel control sequence, dividing the time points in microseconds, and converting the power value ranges of different channels according to the hardware voltage levels. For example, a signal with a power value of 10 watts is converted into a 3.3-volt voltage signal according to the ratio of the hardware drive voltage, and at the same time, the time nodes are accurate to 10-microsecond units. The generated drive control signal is output in the form of a timing file and transmitted to the drive hardware through an electrical interface. The generated drive control signal is loaded by the drive module to drive each channel of the simulated LED light group to emit light. During the light-emitting process, an optical sensor is used to collect the output light intensity of each channel in real time. The sampling frequency of the optical sensor is set to 1 kHz, and the collected data includes the light intensity value and the corresponding time node. For example, at a certain time point, the light intensity value of channel A is 500 lumens, and the light intensity value of channel B is 450 lumens. These feedback data are stored in a matrix file. Each row of the matrix corresponds to a time point, and each column corresponds to the light intensity value of a channel. The feedback strong light value matrix and the target light intensity value matrix are read, and compared row by row according to the time points. The feedback value and the target value at each time point are subtracted to obtain the deviation value. For example, at a certain time point, the target light intensity value is 520 lumens, and the feedback strong light value is 500 lumens, then the light intensity deviation is -20 lumens. The light intensity deviation values of all time points and channels are stored in matrix form. Each item of the matrix represents the light intensity deviation value of a certain channel at the corresponding time point. The feedback optimization module processes the light intensity deviation comparison value matrix and uses an optimization algorithm to adjust the drive signal of each channel to reduce the light intensity deviation. The specific method includes weighted analysis of the light intensity deviation values and calculating the correction coefficient of the drive signal of each channel. For example, the light intensity deviation of a certain channel is -20 lumens, and the corresponding correction coefficient is 0.05. The correction coefficient is superimposed on the drive signal to generate a new drive control signal. The new drive control signal is transmitted to the simulated LED light group, and the adjusted output light intensity is re-collected and compared. This process is repeated until the light intensity deviation value drops below the range of less than 2 lumens, and finally, the feedback optimization and multi-channel adjustment control of the simulated LED light group are completed.

[0145] The above are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather will be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A multi-channel regulation system for LED lamps, characterized in that: The multi-channel adjustment system of the LED lamp includes a parameter acquisition module, a change simulation module, a dead zone compensation module, a phase distribution module, a channel coordination module and a feedback optimization module: The parameter acquisition module is used to: obtain multiple channels of real-time light intensity parameters; The noise covariance of multiple real-time light intensity parameters is estimated to obtain a noise matrix; Perform channel interference analysis on multiple real-time optical intensity parameters to generate a channel crosstalk matrix; The change simulation module is used to: predict the light intensity change of the real-time light intensity parameter based on the noise matrix and the channel crosstalk matrix, and generate a predicted light intensity value; Performing a change simulation process according to the predicted light intensity value to obtain a simulated LED light group; The dead zone compensation module is used to: perform multi-channel duty cycle calculation on the simulated LED light group to obtain a basic duty sequence; Performing dead zone compensation processing on the basic duty sequence to generate an anti-overlap sequence; The phase allocation module is used to: perform phase quantization on the anti-overlapping sequence to obtain the phase quantity; perform phase peak shifting allocation on the anti-overlapping sequence based on the phase quantity to obtain the peak shifting control sequence; The channel coordination module is used to: perform multi-channel temperature compensation on the peak-shifting control sequence to obtain a temperature compensation sequence; perform multi-channel coordination processing according to the temperature compensation sequence to generate a multi-channel control sequence; The feedback optimization module is used to: perform multi-channel adjustment control according to the multi-channel control sequence and collect feedback intensity values ​​at the same time; Feedback optimization is performed on the simulated LED light group based on the feedback intensity value to perform multi-channel adjustment of the LED light.

2. A multi-channel regulation system for LED lamps according to claim 1, characterized in that: The parameter acquisition module is used to: obtain multi-channel real-time light intensity parameters; perform noise covariance estimation on the multi-channel real-time light intensity parameters to obtain a noise matrix; perform channel interference analysis on the multi-channel real-time light intensity parameters to generate a channel crosstalk matrix, which is specifically used for: Get multiple real-time optical parameters; Perform smoothing and filtering on the data of multiple channels of real-time optical parameters to obtain filtered light intensity parameters; Perform state space mapping on the filter light intensity parameters to obtain a light intensity state matrix; The noise covariance of the light intensity state matrix is ​​estimated to obtain a noise matrix; Perform multi-channel topology construction on multi-channel real-time light intensity parameters to obtain the network interference topology structure; Based on the network interference topology, channel crosstalk analysis is performed on multi-channel real-time optical intensity parameters to generate a channel crosstalk matrix.

3. The multi-channel regulation system of an LED lamp according to claim 1, characterized in that: The change simulation module is used to: predict the light intensity change of the real-time light intensity parameter based on the noise matrix and the channel crosstalk matrix to generate a predicted light intensity value; perform change simulation processing according to the predicted light intensity value to obtain a simulated LED light group, specifically used for: Based on the channel crosstalk matrix, the noise matrix is ​​mapped to a channel correlation matrix to obtain a crosstalk correlation matrix; Perform spectrum conversion on real-time light intensity parameters based on the crosstalk correlation matrix to generate a light intensity spectrum matrix; Predict light intensity changes based on the light intensity spectrum matrix to obtain predicted light intensity values; According to the preset basic parameters of the lamp group, the predicted light intensity value is mapped to light intensity parameters to generate corresponding parameters of light intensity; The light group topology is reconstructed based on the parameters corresponding to the light intensity to obtain a simulated LED light group.

4. The multi-channel regulation system of an LED lamp according to claim 1, characterized in that: The dead zone compensation module is used to: perform multi-channel duty cycle calculation on the simulated LED light group to obtain a basic duty sequence; perform dead zone compensation processing on the basic duty sequence to generate an anti-overlap sequence, specifically for: Perform channel brightness equalization processing on the simulated LED light group to obtain balanced light intensity of the light group; Perform multi-channel gamma mapping on the light group to balance the light intensity and generate a balanced linear matrix; Based on the balanced linear matrix, channel interaction compensation is deduced for the simulated LED light group to obtain an interaction compensation matrix; Perform multi-dimensional duty cycle calculation on the interactive compensation matrix to generate a basic duty sequence; Perform edge conflict detection on the basic duty sequence to obtain timing scheduling parameters; Dead zone balance reconstruction is performed based on timing scheduling parameters to generate an anti-overlap sequence.

5. A multi-channel regulation system for LED lamps according to claim 4, characterized in that: The specific steps of performing edge conflict detection on the basic duty sequence to obtain the timing scheduling parameters; performing dead zone balance reconstruction based on the timing scheduling parameters to generate the anti-overlap sequence are: Perform multi-path edge recognition on the basic duty sequence to obtain multi-path switching boundaries; perform switching conflict detection based on the multi-path switching boundaries to generate switching conflict points; Assign priorities to the switching conflict points and obtain timing scheduling parameters; Perform scheduling simulation on the basic duty sequence based on the timing scheduling parameters to obtain the simulated scheduling behavior; Perform dead zone demand analysis on simulated scheduling behavior and generate a set of adjustment intervals; The inter-channel balance compensation is performed on the basic duty sequence according to the adjustment interval set to obtain an anti-overlap sequence.

6. The multi-channel regulation system of an LED lamp according to claim 1, characterized in that: The phase allocation module is used to: perform phase quantization on the anti-overlapping sequence to obtain the phase quantity; perform phase peak shifting allocation on the anti-overlapping sequence based on the phase quantity to obtain the peak shifting control sequence, specifically used to: Performing phase quantization processing on the anti-overlapping sequence to obtain a quantized phase sequence; Perform phase frequency statistics on the phase quantization sequence to generate the phase quantity; Perform power grouping analysis on the anti-overlap sequence based on the number of phases to obtain a phase configuration reference system; The anti-overlapping sequence is interference optimized and rearranged according to the phase configuration reference system to generate a peak-shifting control sequence.

7. A multi-channel regulation system for LED lamps according to claim 6, characterized in that: The specific steps of performing interference optimization rearrangement of the anti-overlap sequence according to the phase configuration reference system to generate the peak shifting control sequence are: Perform channel distribution identification on the anti-overlap sequence in the phase configuration reference system to obtain a channel distribution sequence; The phase clustering region is divided according to the channel distribution sequence to obtain the phase clustering coordinates; Channels are grouped based on phase clustering coordinates to generate a channel grouping set; Performing inter-group interference evaluation on the channel grouping set to generate an inter-channel interference value; Based on the inter-channel interference value, the channel grouping set is screened for rearrangement targets to generate rearrangement target channels; The sequence of the rearrangement target channel is rearranged according to the phase configuration reference system to generate a peak shifting control sequence.

8. The multi-channel regulation system of an LED lamp according to claim 1, characterized in that: The channel coordination module is used to: perform multi-channel temperature compensation on the peak-shifting control sequence to obtain a temperature compensation sequence; perform multi-channel coordination processing according to the temperature compensation sequence to generate a multi-channel control sequence, specifically for: Based on the preset basic parameters of the lamp group, the thermal field gradient mapping is performed on the peak-shifting control sequence to obtain the temperature influence parameters; According to the temperature influence parameter, the channel temperature coefficient compensation is performed on the peak shifting control sequence to obtain the temperature compensation sequence of each channel; Perform compensation integration processing on the temperature compensation sequence of each channel to generate a temperature compensation sequence; Perform channel coordination processing according to the temperature compensation sequence to obtain the channel correction coefficient; perform luminous flux reconstruction based on the channel correction coefficient to generate a light efficiency compensation sequence; The light effect compensation sequence is processed in time domain synchronization to generate a multi-channel control sequence.

9. The multi-channel regulation system of an LED lamp according to claim 1, characterized in that: The feedback optimization module is used to: perform multi-channel adjustment control according to the multi-channel control sequence, and collect the feedback strong light value at the same time; perform feedback optimization on the simulated LED light group based on the feedback strong light value to perform multi-channel adjustment of the LED light, specifically for: Perform hardware timing conversion on multi-channel control sequences to obtain drive control signals; Perform multi-channel adjustment control according to the driving control signal and collect feedback intensity value at the same time; According to the feedback strong light value, the light intensity deviation of the simulated LED light group is compared to obtain the light intensity deviation comparison value; Feedback optimization is performed on the simulated LED lamp group based on the light intensity deviation comparison value to perform multi-channel adjustment of the LED lamp.

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