Multi-channel intelligent control method and system of UV LED light

Abstract

The present application relates to a kind of UV LED light multi-channel intelligent control method and system, comprising the following steps, the light spectrum scanning of the light irradiation area of UV LED light source array is carried out, and the spectral intensity distribution diagram is obtained;Based on the spectral intensity distribution diagram, the light wavelength segmentation division of UV LED light source array is carried out, and a plurality of wavelength subinterval is obtained;For the plurality of wavelength subinterval, the dynamic power regulation of UV LED light source array is carried out, and the optimal power configuration scheme is obtained;According to the optimal power configuration scheme, the each channel synchronous control of UV LED light source array is carried out, and the control signal sequence of the multi-channel collaborative work is obtained;Based on the control signal sequence, the real-time output of UV LED light source array is adjusted, and the stable multi-channel UV LED light output is obtained, solves the technical problem that the spectral output of UV LED light source array has non-uniformity and temperature sensitivity, leading to its possible light intensity distribution uneven, wavelength drift under different working conditions.

Description

Technical Field

[0001] This invention relates to the field of UV LED technology, and in particular to a multi-channel intelligent control method and system for UV LED lights. Background Technology

[0002] With the widespread application of ultraviolet (UV) light in industrial curing, medical disinfection, and biological detection, higher demands are being placed on the precision and controllability of UV light sources. UV LEDs, due to their advantages such as tunable wavelength, long lifespan, fast response speed, and energy efficiency, are gradually becoming an ideal replacement for traditional UV light sources. However, in practical applications, the non-uniformity and temperature sensitivity of the spectral output of UV LED light source arrays can lead to uneven light intensity distribution and wavelength drift under different operating conditions, affecting their stability and reliability in high-precision applications. How to achieve precise and coordinated control of each channel of the UV LED array to ensure the stability and consistency of its output is a key technical challenge that urgently needs to be solved. Summary of the Invention

[0003] The main objective of this invention is to provide a multi-channel intelligent control method for UV LED lights, which solves the technical problem that the non-uniformity and temperature sensitivity of the spectral output of UV LED light source arrays may lead to uneven light intensity distribution and wavelength drift under different working conditions.

[0004] To achieve the above objectives, this invention provides a multi-channel intelligent control method for UV LED lights, applied to UV LED light source arrays, comprising the following steps:

[0005] A spectral intensity distribution map is obtained by scanning the illumination area of ​​the UV LED light source array using a multispectral imaging system.

[0006] Based on the aforementioned spectral intensity distribution map, the UV LED light source array is divided into light wavelength segments to obtain multiple wavelength sub-intervals;

[0007] For the multiple wavelength sub-ranges, the UV LED light source array is dynamically power-adjusted to obtain the optimal power configuration scheme;

[0008] Based on the optimal power configuration scheme, the UV LED light source array is synchronously controlled for each channel to obtain a control signal sequence for multi-channel collaborative operation;

[0009] Based on the control signal sequence, the real-time output to the UV LED light source array is adjusted to obtain a stable multi-channel UV LED light output.

[0010] Furthermore, the step of performing a spectral scan of the illuminated area of ​​the UV LED light source array using a multispectral imaging system to obtain a spectral intensity distribution map includes:

[0011] The wavelength of the illumination area of ​​the UV LED light source array is scanned by a multispectral imaging system to obtain the original spectral data, and the original spectral data is subjected to Fourier transform to obtain frequency domain spectral information.

[0012] The frequency domain spectral information is subjected to adaptive denoising processing to obtain filtered spectral data, and the filtered spectral data is spatially interpolated to obtain density spectral sampling points.

[0013] The density spectral sampling points are subjected to curve fitting to obtain a continuous spectral curve. Based on the continuous spectral curve, the spectral intensity distribution of the UV LED light source array is mapped in two dimensions to obtain a spectral intensity distribution map.

[0014] Furthermore, based on the spectral intensity distribution map, the UV LED light source array is divided into light wavelength segments to obtain multiple wavelength sub-intervals, including:

[0015] Peak features are extracted from the spectral intensity distribution map to obtain a spectral peak sequence, and valley analysis is performed on the spectral peak sequence to obtain wavelength boundary points;

[0016] The spectral intensity distribution map is dynamically segmented based on the wavelength boundary point to obtain an initial band interval, and the spectral energy density of the initial band interval is calculated to obtain a band energy distribution map.

[0017] Cluster analysis is performed on the energy distribution map of the band to obtain the band clustering results, and boundary optimization is performed based on the band clustering results to obtain the optimized wavelength segmentation threshold;

[0018] The UV LED light source array is divided into wavelength sub-intervals based on the optimized wavelength segmentation threshold.

[0019] Furthermore, the step of dynamically segmenting the spectral intensity distribution map based on the wavelength boundary point to obtain the initial band interval includes:

[0020] Interpolation fitting is performed on the wavelength boundary point to obtain the wavelength boundary function, and the wavelength boundary function is differentiated to obtain the wavelength gradient curve;

[0021] Based on the wavelength gradient curve, a dynamic threshold is set on the spectral intensity distribution map to obtain a band segmentation baseline. Based on the band segmentation baseline, the spectral intensity distribution map is divided into regions to obtain preliminary band segmentation results.

[0022] The system detects whether there are overlapping regions in the preliminary band division results. If there are, an overlapping band set is obtained, and the band division baseline is locally adjusted based on the overlapping band set to obtain a corrected band division baseline.

[0023] The spectral intensity distribution map is finally segmented based on the corrected band segmentation baseline to obtain the initial band interval.

[0024] Furthermore, the step of dynamically setting a threshold for the spectral intensity distribution map based on the wavelength gradient curve to obtain a band segmentation baseline includes:

[0025] The extreme points of the wavelength gradient curve are located to obtain a sequence of wavelength jump points, and the spectral energy of the wavelength jump point sequence is accumulated to obtain an energy integral curve.

[0026] The energy integral curve is initially judged by the energy distribution of the spectral band to obtain an initial segmentation threshold group, and the spectral band boundary is located based on the initial segmentation threshold group to obtain a wavelength boundary marker sequence.

[0027] Local peak and valley features are extracted from the wavelength boundary marker sequence to obtain a band feature distribution map, and threshold compensation is calculated based on the band feature distribution map to obtain a dynamic threshold correction amount;

[0028] The initial segmentation threshold group is dynamically adjusted based on the dynamic threshold correction amount to obtain the band segmentation baseline.

[0029] Furthermore, the step of dynamically adjusting the power of the UV LED light source array for the multiple wavelength sub-intervals to obtain the optimal power configuration scheme includes:

[0030] The UV LED light source array is monitored in real time based on the multiple wavelength sub-intervals to obtain the current light intensity data of each sub-interval. The deviation between the current light intensity data and the preset target light intensity distribution is calculated to obtain the light intensity deviation matrix.

[0031] Based on the light intensity deviation matrix, the UV LED light source array is iteratively power-solved to obtain an initial power allocation scheme. Then, the light intensity distribution is simulated based on the initial power allocation scheme to obtain a simulated light intensity distribution map.

[0032] The similarity between the simulated light intensity distribution map and the target light intensity distribution is evaluated to obtain the evaluation index value. Based on the evaluation index value, the initial power allocation scheme is dynamically weighted to obtain the optimal power configuration scheme.

[0033] Furthermore, the iterative power calculation of the UV LED light source array based on the light intensity deviation matrix to obtain the initial power allocation scheme includes:

[0034] The light intensity deviation matrix is ​​subjected to singular value decomposition to obtain the light intensity eigenvector, and a power compensation matrix is ​​constructed based on the light intensity eigenvector to obtain a power adjustment parameter set;

[0035] The power adjustment parameter set is recursively calculated using the Hamiltonian optimization method to obtain a power iteration sequence, and the convergence of the power iteration sequence is analyzed to obtain a steady-state power solution set.

[0036] Multi-objective optimization is performed on the steady-state power solution set to obtain the Pareto optimal solution set, and power configuration evaluation is performed based on the Pareto optimal solution set to obtain candidate power schemes;

[0037] Temperature field simulation was performed on the UV LED light source array based on the candidate power scheme to obtain the chip junction temperature distribution map in the UV LED light source array. Power correction was then performed based on the chip junction temperature distribution map to obtain the initial power allocation scheme.

[0038] Furthermore, the step of synchronously controlling each channel of the UV LED light source array according to the optimal power configuration scheme to obtain a control signal sequence for multi-channel coordinated operation includes:

[0039] The optimal power configuration scheme is mapped by pulse frequency to obtain a reference pulse sequence, and the reference pulse sequence is phase-compensated to obtain a channel drive timing table.

[0040] The channel drive timing table is expanded into multiple pulses using time-division multiplexing technology to obtain an initial modulation waveform group. Dead time compensation is then performed based on the initial modulation waveform group to obtain an anti-collision drive sequence.

[0041] The anti-collision drive sequence is subjected to current response characteristic analysis to obtain a channel current prediction spectrum, and waveform shaping is performed based on the channel current prediction spectrum to obtain a modified modulation sequence.

[0042] Based on the modified modulation sequence, channel synchronization control is performed on the UV LED light source array to obtain a control signal sequence for multi-channel collaborative operation.

[0043] This invention also provides a multi-channel intelligent control system for UV LED lights, applied to a UV LED light source array, comprising:

[0044] The scanning module is used to perform spectral scanning on the illuminated area of ​​the UV LED light source array using a multispectral imaging system to obtain a spectral intensity distribution map;

[0045] The segmentation module is used to divide the UV LED light source array into light wavelength segments based on the spectral intensity distribution map, thereby obtaining multiple wavelength sub-intervals;

[0046] The adjustment module is used to dynamically adjust the power of the UV LED light source array for the multiple wavelength sub-ranges to obtain the optimal power configuration scheme;

[0047] The control module is used to synchronously control each channel of the UV LED light source array according to the optimal power configuration scheme, so as to obtain a control signal sequence for multi-channel coordinated operation;

[0048] The output module is used to adjust the real-time output of the UV LED light source array based on the control signal sequence to obtain a stable multi-channel UV LED light output.

[0049] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of any of the methods described above.

[0050] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of any of the methods described above.

[0051] This invention provides a multi-channel intelligent control method for UV LED lights, comprising the following steps: spectral scanning of the illumination area of ​​a UV LED light source array to obtain a spectral intensity distribution map; dividing the UV LED light source array into wavelength segments based on the spectral intensity distribution map to obtain multiple wavelength sub-intervals; dynamically adjusting the power of the UV LED light source array for the multiple wavelength sub-intervals to obtain an optimal power configuration scheme; synchronously controlling each channel of the UV LED light source array according to the optimal power configuration scheme to obtain a control signal sequence for multi-channel collaborative operation; and adjusting the real-time output of the UV LED light source array based on the control signal sequence to obtain a stable multi-channel UV LED light output. This method solves the technical problem that the non-uniformity and temperature sensitivity of the spectral output of UV LED light source arrays may lead to uneven light intensity distribution and wavelength drift under different operating conditions. It achieves synchronous control of each channel of the UV LED light source array using pulse width modulation technology, ensuring collaborative operation between multiple channels. This approach not only ensures the consistency of output between channels but also effectively reduces potential interference between channels, thereby providing a more stable lighting effect. Attached Figure Description

[0052] Figure 1 This is a schematic diagram of the steps of a multi-channel intelligent control method for UV LED lights in one embodiment of the present invention;

[0053] Figure 2 This is a structural block diagram of a multi-channel intelligent control system for UV LED lights in one embodiment of the present invention;

[0054] Figure 3 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present invention.

[0055] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0057] like Figure 1 As shown, Figure 1 This invention provides a multi-channel intelligent control method for UV LED lights, applied to a UV LED light source array, comprising the following steps:

[0058] Step S1: The light-irradiated area of ​​the UV LED light source array is scanned using a multispectral imaging system to obtain a spectral intensity distribution map.

[0059] Specifically, in implementing a multi-channel intelligent control method for UV LED lights, the first step is to use a multispectral imaging system to scan the spectral area of ​​the UV LED light source array to obtain a spectral intensity distribution map. This process is fundamental to the entire method. Specifically, the multispectral imaging system captures image information at different wavelengths. It utilizes specific optical components and sensors to separate and record the intensity of light from the UV LED light source array at various wavelengths. For example, in industrial curing processes, different materials may require different wavelengths and intensities of ultraviolet light for effective curing. Therefore, accurately obtaining the spectral intensity distribution of the UV LED light source array irradiating the workpiece surface is crucial. The multispectral imaging system can precisely identify the light intensity within each wavelength sub-interval, which not only aids in wavelength segmentation in subsequent steps but also provides a basis for dynamic power adjustment. For instance, in a scenario where UV LED light is used to cure printing ink, the multispectral imaging system can analyze in detail the spectral characteristics irradiated on the surface of the ink to be cured, including which wavelength ranges have stronger or weaker energy. Based on this data, the light source output can be optimized and adjusted to ensure that each part receives the most suitable illumination conditions for curing, thereby improving curing efficiency and quality while reducing energy waste. Therefore, this step is crucial for achieving stable and efficient multi-channel UV LED light output.

[0060] Step S2: Based on the spectral intensity distribution map, the UV LED light source array is divided into light wavelength segments to obtain multiple wavelength sub-intervals.

[0061] Specifically, after obtaining the spectral intensity distribution map scanned by the multispectral imaging system, the next step is to divide the UV LED light source array into wavelength segments, thus obtaining multiple wavelength sub-intervals. This process is a key step in realizing multi-channel intelligent control. Specifically, by analyzing the data in the spectral intensity distribution map, the trend of light intensity variation within different wavelength ranges is identified. Based on the set wavelength division strategy or target application requirements, the entire spectral range is divided into several representative wavelength sub-intervals. This division method not only reflects the output characteristics of the UV LED light source array at different wavelengths but also provides a basis for subsequent dynamic power adjustment for each sub-interval. For example, in industrial curing scenarios, different ink materials have different absorption efficiencies for specific wavelengths of ultraviolet light. Therefore, it is necessary to divide the output band of the UV LED light source into multiple sub-intervals based on the spectral intensity distribution map, such as 365nm, 385nm, and 405nm bands. Each sub-interval corresponds to the optimal curing wavelength for different types of inks. This wavelength segmentation allows for independent control and optimization of each wavelength sub-range, ensuring that each type of material receives optimal light energy within its most suitable wavelength range. This improves overall curing performance and production efficiency while avoiding energy waste and potential damage caused by broadband irradiation. Therefore, wavelength segmentation based on spectral intensity distribution not only enhances the system's controllability and accuracy but also lays a solid foundation for subsequent dynamic power adjustment and multi-channel collaborative control.

[0062] Step S3: For the multiple wavelength sub-ranges, dynamically adjust the power of the UV LED light source array to obtain the optimal power configuration scheme.

[0063] Specifically, the process of dynamically adjusting the power of the UV LED light source array across multiple wavelength sub-ranges to obtain the optimal power configuration is a core step in ensuring the efficient operation of the multi-channel intelligent control method. After determining each wavelength sub-range, the next task is to precisely adjust the power of the UV LED light source in each sub-range based on the requirements of the actual application scenario and the information provided by the spectral intensity distribution map. This step first requires analyzing the actual light intensity requirements within each wavelength sub-range. For example, in industrial curing processes, the optimal absorption efficiency of different materials for ultraviolet light often corresponds to specific wavelengths and light intensities. Therefore, by dynamically adjusting the power output of each wavelength sub-range, the UV LED light source array can be made more closely suited to these specific requirements. For instance, when processing printed materials containing multiple materials, some inks may cure best at a wavelength of 365nm, while others perform better at wavelengths of 385nm or 405nm. In this case, power optimization needs to be performed separately for these wavelength sub-ranges to ensure that each ink can complete the curing process under its most suitable illumination conditions. This process involves real-time monitoring of the light intensity in each wavelength sub-range and continuous adjustment of power settings based on a feedback mechanism until the optimal power configuration is found that meets curing quality requirements while minimizing energy consumption. This approach not only improves the curing quality of the final product but also effectively reduces energy consumption and extends the lifespan of the UV LED light source, thus bringing significant economic benefits and technological advantages to industrial production. Throughout the process, the application of dynamic power adjustment technology allows the UV LED light source array to flexibly adapt to different operating conditions, ensuring consistent and stable output.

[0064] Step S4: According to the optimal power configuration scheme, perform synchronous control of each channel of the UV LED light source array to obtain a control signal sequence for multi-channel collaborative operation.

[0065] Specifically, according to the optimal power configuration scheme, synchronous control of each channel of the UV LED light source array is performed to obtain a control signal sequence for multi-channel collaborative operation. This is a key step in achieving coordinated operation and output consistency across multiple channels in the entire intelligent control method. The core of this step lies in converting the optimal power parameters obtained in the previous step into specific drive control signals, and using pulse width modulation (PWM) technology to precisely adjust the time duty cycle of each UV LED light source channel, thereby achieving precise control of the output intensity in each wavelength sub-interval. Since UV LED light source arrays typically consist of multiple independently controllable light-emitting units, each unit corresponding to one or more wavelength sub-intervals, it is essential to ensure that all channels maintain a high degree of synchronization in the time dimension to avoid problems such as uneven illumination and energy fluctuations. For example, in industrial curing applications, when multiple ink materials are simultaneously irradiated with UV light of different wavelengths, if there is a control delay or output asynchrony between channels, some areas may not cure completely or may be overexposed, affecting product quality. Therefore, by using PWM technology to synchronously modulate each channel, not only can we ensure that each channel outputs stably according to the optimal power configuration, but we can also achieve collaborative work between multiple channels, so that the entire UV LED light source array forms a unified, uniform and efficient illumination field in space and time, thereby improving the overall control accuracy and application stability of the system.

[0066] Step S5: Based on the control signal sequence, adjust the real-time output of the UV LED light source array to obtain a stable multi-channel UV LED light output.

[0067] Specifically, adjusting the real-time output of the UV LED light source array based on the aforementioned control signal sequence is a key execution step in realizing the multi-channel intelligent control method and achieving stable multi-channel UV LED light output. The core of this step lies in converting the control signal sequence generated in the previous step using pulse width modulation technology into actual light source driving commands, which are then applied to each independent light-emitting channel in the UV LED light source array. This allows for dynamic control of the output intensity and temporal characteristics of each channel. During this process, the system continuously monitors the actual output status of each channel and adjusts it based on the target power set by the control signal sequence to ensure consistent and stable output under different operating conditions. For example, in industrial curing applications, when multiple ink materials need to be simultaneously irradiated with different wavelengths of ultraviolet light, the system precisely drives each channel according to the control signal sequence, ensuring that the UV LED light sources in each wavelength sub-range operate at the optimal power configuration. A real-time closed-loop feedback mechanism promptly corrects output deviations caused by temperature fluctuations or device aging. This not only ensures the spatial uniformity and temporal continuity of light intensity between channels but also significantly improves the overall system's response speed and anti-interference capability. Through this process, efficient, stable, and multi-channel coordinated output control of the UV LED light source array was finally achieved, meeting the urgent needs of high-precision industrial applications for refined management of the light source system.

[0068] In a specific embodiment, the step of performing a spectral scan of the illuminated area of ​​the UV LED light source array using a multispectral imaging system to obtain a spectral intensity distribution map includes:

[0069] The wavelength of the illumination area of ​​the UV LED light source array is scanned by a multispectral imaging system to obtain the original spectral data, and the original spectral data is subjected to Fourier transform to obtain frequency domain spectral information.

[0070] The frequency domain spectral information is subjected to adaptive denoising processing to obtain filtered spectral data, and the filtered spectral data is spatially interpolated to obtain density spectral sampling points.

[0071] The density spectral sampling points are subjected to curve fitting to obtain a continuous spectral curve. Based on the continuous spectral curve, the spectral intensity distribution of the UV LED light source array is mapped in two dimensions to obtain a spectral intensity distribution map.

[0072] Specifically, in the process of "scanning the spectrum of the UV LED light source array's illumination area using a multispectral imaging system to obtain a spectral intensity distribution map," this step involves not only basic data acquisition but also a series of sophisticated data processing and modeling procedures to ensure that the final spectral intensity distribution map possesses key characteristics such as high precision, high resolution, and spatial continuity. Specifically, this process first relies on the hardware performance of the multispectral imaging system and its wavelength scanning capability over the UV LED light source array's illumination area. This system acquires raw spectral data, i.e., the light intensity information received by each pixel at different wavelengths. Since the raw spectral data often contains noise interference, it needs to be further subjected to Fourier transform to convert the signal in the time or spatial domain into frequency domain spectral information. This makes it easier to identify the dominant frequency components and anomalous frequency components, improving the accuracy of subsequent processing. Next, after completing the frequency domain transformation, the system performs adaptive denoising processing on the frequency domain spectral information. The core of this step is to use digital filtering technology to remove high-frequency noise or stray signals from non-target wavelengths while retaining the effective spectral characteristics within the main wavelength range. The advantage of adaptive denoising lies in its ability to dynamically adjust filtering parameters based on the noise level of the current environment. This avoids the over-smoothing or under-filtering problems that may arise from traditional fixed filtering, ensuring that the filtered spectral data maintains its original trend while achieving a higher signal-to-noise ratio. Subsequently, to construct a complete spectral image, spatial interpolation is performed on the filtered spectral data to fill in the blank areas caused by insufficient sensor sampling density, thereby generating density spectral sampling points. These sampling points cover the entire illuminated area and exhibit a relatively uniform and dense distribution in the spatial dimension. Based on this, the system further employs curve fitting to mathematically model the density spectral sampling points, fitting a continuous spectral curve. This curve accurately describes the light intensity variation trend corresponding to different wavelengths at each spatial location. Its mathematical expression is typically based on methods such as polynomial regression or spline interpolation. Curve fitting not only enhances the continuity and smoothness of the spectral data but also improves the resolution of the data in the wavelength direction, making subsequent analysis more accurate and reliable. Finally, based on the aforementioned continuous spectral curve, the system maps it two-dimensionally to the spatial coordinate system of the UV LED light source array, establishing a correspondence between spatial coordinates and spectral intensity, thereby generating the final spectral intensity distribution map. This image not only shows the spatial distribution of light intensity at various wavelengths, but also provides a visual and quantifiable basis for subsequent wavelength segmentation and power adjustment. For example, in industrial curing applications, when multiple ink materials need to be irradiated with ultraviolet light to complete the curing reaction, this spectral intensity distribution map can clearly reflect the composition and intensity distribution of light wavelengths in different regions.Assuming that the ink in a certain area responds best to a 385nm wavelength, but the actual 385nm light intensity illuminating that area is weak, the system can quickly identify this problem based on the spectral intensity distribution map and optimize the power of that wavelength sub-range in subsequent steps. Furthermore, since this image is a two-dimensional distribution model constructed based on spatial coordinates, it can also be used to determine whether there are uneven illumination phenomena such as vignetting or excessive brightness in the center, thus providing data support for optimizing the layout of the light source array. In summary, by sequentially performing Fourier transform, adaptive denoising, spatial interpolation, curve fitting, and finally two-dimensional mapping to form a spectral intensity distribution map from the raw spectral data acquired by the multispectral imaging system, this series of steps constitutes a complete closed-loop process from raw data acquisition to high-quality image generation. This process not only improves the sensing capability and control accuracy of the UVLED light source array in multi-channel intelligent control but also lays a solid technical foundation for achieving refined management in complex lighting environments, making it an indispensable and crucial link in the entire control method.

[0073] In a specific embodiment, based on the spectral intensity distribution map, the UV LED light source array is divided into light wavelength segments to obtain multiple wavelength sub-intervals, including:

[0074] Peak features are extracted from the spectral intensity distribution map to obtain a spectral peak sequence, and valley analysis is performed on the spectral peak sequence to obtain wavelength boundary points;

[0075] The spectral intensity distribution map is dynamically segmented based on the wavelength boundary point to obtain an initial band interval, and the spectral energy density of the initial band interval is calculated to obtain a band energy distribution map.

[0076] Cluster analysis is performed on the energy distribution map of the band to obtain the band clustering results, and boundary optimization is performed based on the band clustering results to obtain the optimized wavelength segmentation threshold;

[0077] The UV LED light source array is divided into wavelength sub-intervals based on the optimized wavelength segmentation threshold.

[0078] Specifically, in the process of "dividing the UV LED light source array into multiple wavelength sub-intervals based on the spectral intensity distribution map," the core of this step lies in using systematic data analysis to transform continuous spectral intensity information into discrete wavelength sub-intervals with clear physical meaning. This provides a precise wavelength division basis for subsequent dynamic power adjustment and multi-channel collaborative control. This process first relies on extracting key feature information from the spectral intensity distribution map. Specifically, the system identifies the spectral peak sequences in the map, i.e., the wavelength points corresponding to the maximum light intensity at different spatial locations. Further, it performs valley analysis on these peak sequences to find the minimum value regions between adjacent peaks; these regions are considered potential wavelength boundary points. Each wavelength boundary point not only contains a wavelength critical value but also corresponds to a spectral intensity threshold, used to define the energy boundary between two adjacent wavelength bands. Subsequently, after obtaining the initial wavelength boundary points, the system dynamically divides the original spectral intensity distribution map based on these boundary points, forming several initial wavelength band intervals. The key to this step is ensuring that the division of each wavelength band interval conforms to the actual trend of light energy variation while also taking into account the specific needs of different application scenarios. For example, in industrial curing scenarios, some ink materials have higher absorption efficiency for ultraviolet light within a specific wavelength range. Therefore, it is necessary to rationally set the start and end positions of the bands based on the spectral distribution. To further evaluate the energy characteristics of each initial band interval, the system also calculates the spectral energy density of each band, generating a band energy distribution map. This map clearly shows the ultraviolet energy carried per unit area within different wavelength intervals, helping to determine which bands are more practically valuable under current application conditions. Based on this, the system further performs cluster analysis on the band energy distribution map. The purpose is to identify band groups with similar energy distribution patterns and optimize the band division boundaries accordingly. Cluster analysis typically uses unsupervised learning algorithms such as K-means and DBSCAN to group bands with similar energy characteristics into one category, thereby revealing deeper structural information. For example, in an industrial curing task, it may be found that the energy distribution patterns of the 365nm and 385nm bands are relatively similar, while the 405nm band exhibits completely different energy characteristics. In this case, the clustering results will help the system identify this difference and adjust the band division strategy accordingly. After clustering, the system refines the boundaries of each category to eliminate band overlap or breakage caused by improper initial boundary point selection, ultimately obtaining optimized wavelength segmentation thresholds. These thresholds serve as the baseline for final band division. Finally, based on these optimized wavelength segmentation thresholds, the system performs wavelength partitioning on the entire UV LED light source array, dividing the originally continuous spectral output into multiple clearly defined wavelength sub-intervals. Each wavelength sub-interval corresponds to a set of independently controllable light source channels, enabling subsequent dynamic power adjustment to be optimized separately for different wavelength bands.For example, in practical applications of printing ink curing, if a certain area primarily uses inks that respond well to 385nm ultraviolet light, the system can enhance the output only in that wavelength sub-range, while appropriately reducing power in other bands to save energy and avoid unnecessary material damage. This wavelength partitioning mechanism not only improves the control precision and energy efficiency of the UV LED light source system but also effectively meets the personalized spectral output requirements of complex application scenarios. In summary, this step constructs a complete wavelength partitioning process system through multiple technical steps, including extracting spectral peaks, analyzing valley characteristics, dynamically segmenting bands, calculating energy density, cluster analysis, and boundary optimization. It not only achieves fine segmentation of the output spectrum of the UV LED light source array but also provides a solid data foundation and decision support for subsequent multi-channel intelligent control. In typical application scenarios such as industrial curing, this wavelength segmentation method based on spectral intensity distribution maps can significantly improve curing efficiency, product quality, and energy utilization, fully demonstrating its technical advantages and application value in the field of high-precision illumination control.

[0079] In a specific embodiment, the step of dynamically segmenting the spectral intensity distribution map based on the wavelength boundary point to obtain an initial band interval includes:

[0080] Interpolation fitting is performed on the wavelength boundary point to obtain the wavelength boundary function, and the wavelength boundary function is differentiated to obtain the wavelength gradient curve;

[0081] Based on the wavelength gradient curve, a dynamic threshold is set on the spectral intensity distribution map to obtain a band segmentation baseline. Based on the band segmentation baseline, the spectral intensity distribution map is divided into regions to obtain preliminary band segmentation results.

[0082] The system detects whether there are overlapping regions in the preliminary band division results. If there are, an overlapping band set is obtained, and the band division baseline is locally adjusted based on the overlapping band set to obtain a corrected band division baseline.

[0083] The spectral intensity distribution map is finally segmented based on the corrected band segmentation baseline to obtain the initial band interval.

[0084] Specifically, in the process of "dynamically segmenting the spectral intensity distribution map based on the wavelength boundary points to obtain initial band intervals," this step involves transforming the extracted wavelength boundary points into operable band division criteria. Through mathematical modeling and image processing techniques, the final initial band intervals are ensured to have good boundary clarity, energy concentration, and spatial consistency. Specifically, this process first requires interpolating and fitting the wavelength boundary points extracted from the spectral intensity distribution map to construct a wavelength boundary function that describes the boundary trend across the entire wavelength range. Since wavelength boundary points are typically discrete, mathematical methods such as spline interpolation, polynomial regression, or radial basis functions must be used to extend them into a continuous functional expression, thereby forming an accurate description of the overall output spectrum variation trend of the light source array. After obtaining the wavelength boundary function, the system further differentiates it, calculating its first derivative to generate a wavelength gradient curve. This curve reflects the rate of change of the wavelength boundary function at different locations, i.e., the steepness of the wavelength transition region. The introduction of wavelength gradient curves helps identify regions in the spectrum where energy changes drastically; these regions often correspond to the boundaries between two adjacent bands. Analysis of this curve allows for more precise setting of band segmentation baselines, avoiding missegmentation caused by manually setting fixed thresholds. For example, in industrial curing scenarios, when multiple ink materials coexist, the intensity changes in certain wavelength ranges may be relatively gradual. Using traditional fixed threshold methods can easily lead to inaccurate band segmentation, affecting subsequent power adjustment. Adaptively setting the segmentation baseline using wavelength gradient curves effectively improves the robustness and accuracy of band segmentation. Next, the system divides the spectral intensity distribution map into regions based on the aforementioned band segmentation baseline, generating preliminary band segmentation results. The key to this step is using image segmentation algorithms (such as thresholding, edge detection, or region growing) to divide the spectral intensity distribution map into several non-overlapping regions, each representing a potential band. However, in practical applications, due to potential local overlap in spectral intensity distributions, especially when energy transitions between adjacent bands are relatively gradual, spectral information from different bands may interpenetrate, forming so-called overlapping regions. At this point, the system will automatically detect whether there are such overlapping regions in the preliminary band division results and classify these regions into overlapping band sets. Once an overlapping band set is found, the system will make local adjustments to the original band division baseline based on the set to eliminate the overlap and optimize the band boundaries. This local adjustment usually adopts a sliding window mechanism, combining the trend of spectral intensity changes in the neighborhood to dynamically correct the segmentation threshold, making the originally blurred boundaries clearer.For example, during the curing process of printing ink, if a certain area is simultaneously affected by both the 365nm and 385nm wavelength bands, causing an overlap in the energy distribution of these two bands, the system will recalculate the optimal segmentation point for that area based on the information from the overlapping band set. This ensures that each band only covers its assigned illumination range, avoiding energy waste or material damage. Finally, the system re-segments the spectral intensity distribution map based on the corrected band segmentation baseline, generating initial band intervals. These intervals not only have clear start and end wavelength boundaries but also maintain consistent illumination characteristics in space, providing a reliable input basis for subsequent dynamic power adjustment and multi-channel collaborative control. Throughout the entire process, the establishment of wavelength boundary functions, analysis of wavelength gradient curves, setting of band segmentation baselines, and detection and correction of overlapping areas are closely linked, forming a complete dynamic band segmentation system that significantly improves the control accuracy and applicability of UV LED light source arrays in complex illumination environments. For example, in an industrial curing equipment using multiple UV-sensitive materials, the system precisely divides the spectral output of the UV LED light source array using the method described above. This ensures that each material can receive high-intensity irradiation within its optimal absorption wavelength range, thereby significantly improving curing efficiency and product quality. Simultaneously, the adoption of dynamic segmentation and local correction strategies avoids energy conflicts caused by overlapping wavelengths, achieving efficient, energy-saving, and stable multi-channel light source control.

[0085] In a specific embodiment, the step of dynamically setting a threshold for the spectral intensity distribution map based on the wavelength gradient curve to obtain a band segmentation baseline includes:

[0086] The extreme points of the wavelength gradient curve are located to obtain a sequence of wavelength jump points, and the spectral energy of the wavelength jump point sequence is accumulated to obtain an energy integral curve.

[0087] The energy integral curve is initially judged by the energy distribution of the spectral band to obtain an initial segmentation threshold group, and the spectral band boundary is located based on the initial segmentation threshold group to obtain a wavelength boundary marker sequence.

[0088] Local peak and valley features are extracted from the wavelength boundary marker sequence to obtain a band feature distribution map, and threshold compensation is calculated based on the band feature distribution map to obtain a dynamic threshold correction amount;

[0089] The initial segmentation threshold group is dynamically adjusted based on the dynamic threshold correction amount to obtain the band segmentation baseline.

[0090] Specifically, in the process of "dynamically setting a threshold for the spectral intensity distribution map based on the wavelength gradient curve to obtain a band segmentation baseline," this step is a crucial link in the entire wavelength segmentation process. Its purpose is to establish a dynamic threshold mechanism that can adapt to different lighting conditions and material response characteristics by deeply analyzing the energy change characteristics contained in the wavelength gradient curve and combining energy integration with local peak and valley information. This provides a precise segmentation basis for subsequent band division. Specifically, this process first requires locating extreme points on the generated wavelength gradient curve to identify a sequence of wavelength transition points with significant changes. These transition points typically correspond to the positions where the energy transition between two adjacent bands is most drastic, marking key nodes where spectral characteristics undergo abrupt changes. Based on this, the system further performs spectral energy accumulation calculations on the wavelength transition point sequence, that is, accumulating the corresponding light intensity values ​​point by point along the wavelength direction, ultimately forming an energy integration curve. This curve intuitively reflects the changing trend of ultraviolet light energy accumulated per unit area as the wavelength increases throughout the entire spectrum. For example, in industrial curing applications, some ink materials may absorb a large amount of ultraviolet energy within a specific wavelength range. In this case, the energy integral curve will show a significant inflection point, which serves as a crucial reference for setting the initial segmentation threshold. By analyzing the energy integral curve, the system can initially determine which regions have high energy concentration and which regions are relatively sparse, thus providing a basis for the next step of threshold determination. Subsequently, the system uses spectral energy distribution information to perform initial threshold determination on the energy integral curve, generating an initial segmentation threshold set. The core of this step lies in determining a reasonable set of initial thresholds based on a pre-set energy distribution model or historical experience data, ensuring that each threshold roughly corresponds to the boundary of an energy concentration region. For example, in a printing curing task, if the energy integral curve between 365nm and 385nm shows a rapid upward trend, it indicates that this band may be the main curing band for a certain type of ink. In this case, a higher initial segmentation threshold can be set for this region to ensure that the band is completely preserved. However, since initial thresholds often lack consideration for local details, further optimization is needed by combining wavelength boundary marker sequences. Next, the system extracts local peak and valley features from the wavelength boundary marker sequence generated by the initial segmentation threshold set, constructing a band feature distribution map. This map not only shows the spatial energy distribution of each band but also reveals the local peak and valley regions within each band. By analyzing these features, the system can identify whether the current threshold setting is reasonable and whether there are missegmentation problems caused by local fluctuations. For example, if the local peak and valley differences of a certain band are large, it indicates that there may be multiple substructures within that band. In this case, the segmentation threshold should be adjusted appropriately to avoid incorrectly classifying areas with large energy differences into the same band.Based on the aforementioned band feature distribution map, the system further performs threshold compensation calculations to derive a dynamic threshold correction amount. The introduction of this correction amount allows the system to fine-tune the initial segmentation threshold according to the actual spectral characteristics, thereby improving the accuracy and robustness of band division. For example, in complex curing scenarios with multiple materials coexisting, if a significant uneven energy distribution is found in a certain area, the system can automatically adjust the segmentation threshold based on the local characteristics of that area to ensure that the energy distribution within each band is as uniform as possible, improving the effectiveness of subsequent power adjustment. Finally, the system dynamically adjusts the initial segmentation threshold set based on this dynamic threshold correction amount, generating the final band division baseline. This baseline is not only the direct basis for subsequent band division but also the fundamental support for realizing multi-channel intelligent control of the UV LED light source array. Through this baseline, the system can ensure that each band has a clear start and end wavelength range and maintains good consistency and stability in both spatial and wavelength dimensions. In summary, this step, through multiple technical steps such as extreme point location, energy integral curve construction, initial segmentation threshold setting, band feature extraction, and dynamic threshold correction, achieves adaptive identification and optimization of band boundaries in the spectral intensity distribution map. It not only improves the accuracy and flexibility of band division, but also provides a solid data foundation for subsequent multi-channel collaborative control. In typical application scenarios such as industrial curing, this dynamic threshold setting method based on wavelength gradient curves can significantly improve curing efficiency, product quality, and energy utilization, fully demonstrating its technological advantages and application value in the field of high-precision illumination control.

[0091] In a specific embodiment, the step of dynamically adjusting the power of the UV LED light source array for the multiple wavelength sub-intervals to obtain an optimal power configuration scheme includes:

[0092] The UV LED light source array is monitored in real time based on the multiple wavelength sub-intervals to obtain the current light intensity data of each sub-interval. The deviation between the current light intensity data and the preset target light intensity distribution is calculated to obtain the light intensity deviation matrix.

[0093] Based on the light intensity deviation matrix, the UV LED light source array is iteratively power-solved to obtain an initial power allocation scheme. Then, the light intensity distribution is simulated based on the initial power allocation scheme to obtain a simulated light intensity distribution map.

[0094] The similarity between the simulated light intensity distribution map and the target light intensity distribution is evaluated to obtain the evaluation index value. Based on the evaluation index value, the initial power allocation scheme is dynamically weighted to obtain the optimal power configuration scheme.

[0095] Specifically, in the process of "dynamically adjusting the power of the UV LED light source array for the multiple wavelength sub-intervals to obtain the optimal power configuration scheme," this step is one of the most critical control links in the entire multi-channel intelligent control method. Its core objective is to ensure that the light intensity output of each wavelength sub-interval can accurately match the preset target light intensity distribution through real-time feedback and optimization calculation mechanisms, thereby improving the overall system's illumination accuracy, energy utilization, and environmental adaptability. Specifically, this process first relies on real-time light intensity monitoring of the UV LED light source array based on the divided multiple wavelength sub-intervals. The system uses high-precision photoelectric sensors or detection modules integrated into the control system to continuously collect the current light intensity data of each sub-interval. This data not only includes the light intensity values ​​of each wavelength sub-interval at different spatial locations but also covers the changing trends over time, forming the basis for subsequent deviation analysis. Based on this, the system compares the collected current light intensity data with the preset target light intensity distribution point by point, calculates the difference between the two, and then generates a light intensity deviation matrix. This matrix records the light intensity deviation between each wavelength sub-interval and the target value at each spatial location in a two-dimensional form, providing a quantitative basis for subsequent power adjustment. For example, in industrial curing applications, if the ink material in a certain area has the highest absorption efficiency for 385nm ultraviolet light, then the target light intensity for that band in that area should be set to a higher level. If actual monitoring results show that the light intensity in that band is too low, this will be reflected as a negative deviation in the deviation matrix, indicating that the system needs to enhance the power of that band. Subsequently, the system iteratively solves for the power of the UVLED light source array based on the aforementioned light intensity deviation matrix, employing mathematical optimization algorithms such as gradient descent, least squares optimization, or neural network approximation to gradually approximate the optimal power allocation scheme that satisfies the target light intensity distribution. This initial power allocation scheme contains suggested power output values ​​for each wavelength sub-interval at different spatial locations, reflecting the optimal control strategy of the system under the current state. To verify the effectiveness of this initial power allocation scheme, the system further performs light intensity distribution simulation based on this scheme, simulating the actual illumination effect of the UV LED light source array under this power configuration and generating a simulated light intensity distribution map. This map not only shows the expected distribution of illumination intensity in spatial and wavelength dimensions but also provides a comparable reference standard for subsequent evaluation. Next, the system evaluates the similarity between the simulated light intensity distribution map and the target light intensity distribution. This is typically achieved using metrics such as the Structural Similarity Index (SSIM), Mean Square Error (MSE), or Pearson correlation coefficient to measure the degree of matching between the two, ultimately yielding an evaluation index value. This index value directly reflects the control accuracy and stability of the current power allocation scheme. A good evaluation result indicates that the current scheme is close to optimal; otherwise, further optimization and adjustment are needed.Based on this evaluation index, the system dynamically adjusts the initial power allocation scheme by assigning different adjustment priorities and correction magnitudes according to the importance of different wavelength sub-intervals in the overall illumination task and their current deviation magnitude, thus forming the final optimal power configuration scheme. For example, in the practical application of printing ink curing, if the system detects that the light intensity of a certain wavelength band (e.g., 405nm) is consistently lower than the target value in some areas, and the evaluation index shows that this deviation significantly affects the overall curing quality, the system will increase the power weight of the spatial region corresponding to that wavelength band in the optimal power configuration scheme, while appropriately reducing the output of other non-critical wavelength bands to ensure reasonable energy allocation and efficient utilization. This dynamic adjustment mechanism not only improves the system's response speed and control accuracy but also enhances its adaptability under complex illumination conditions. In summary, this step, through multiple technical steps such as real-time light intensity monitoring, deviation matrix construction, iterative power solution, light intensity distribution simulation, similarity evaluation, and dynamic weight adjustment, constructs a closed-loop feedback and continuously optimized dynamic power adjustment system. It not only achieves precise control over each wavelength sub-range of the UV LED light source array, but also provides stable and reliable power support for multi-channel collaborative operation, making it an indispensable core component of the entire intelligent control method. In typical application scenarios such as industrial curing, this optimization mechanism based on dynamic power adjustment can significantly improve product quality, curing efficiency, and energy utilization, fully demonstrating its technological advantages and engineering value in the field of high-precision illumination control.

[0096] In a specific embodiment, the iterative power calculation of the UV LED light source array based on the light intensity deviation matrix to obtain an initial power allocation scheme includes:

[0097] The light intensity deviation matrix is ​​subjected to singular value decomposition to obtain the light intensity eigenvector, and a power compensation matrix is ​​constructed based on the light intensity eigenvector to obtain a power adjustment parameter set;

[0098] The power adjustment parameter set is recursively calculated using the Hamiltonian optimization method to obtain a power iteration sequence, and the convergence of the power iteration sequence is analyzed to obtain a steady-state power solution set.

[0099] Multi-objective optimization is performed on the steady-state power solution set to obtain the Pareto optimal solution set, and power configuration evaluation is performed based on the Pareto optimal solution set to obtain candidate power schemes;

[0100] Temperature field simulation was performed on the UV LED light source array based on the candidate power scheme to obtain the chip junction temperature distribution map in the UV LED light source array. Power correction was then performed based on the chip junction temperature distribution map to obtain the initial power allocation scheme.

[0101] Specifically, in the process of "iteratively solving the power of the UV LED light source array based on the light intensity deviation matrix to obtain an initial power allocation scheme," this step is the core link in the mathematical modeling and optimization control of the entire dynamic power adjustment mechanism. Its purpose is to construct a power allocation model that can balance illumination accuracy and system stability by introducing high-order matrix analysis, optimization algorithms, and thermodynamic simulation methods, thereby providing a reliable initial solution for subsequent optimal power configuration. Specifically, this process first requires performing singular value decomposition (SVD) on the obtained light intensity deviation matrix. This is a classic matrix dimensionality reduction technique that can extract key light intensity feature vectors while preserving the main error characteristics. These feature vectors not only reflect the difference patterns between the current wavelength sub-intervals and the target light intensity but also reveal the error distribution patterns at different spatial locations. Based on this, the system will construct a power compensation matrix based on the aforementioned light intensity feature vectors and further generate a power adjustment parameter set. This parameter set contains suggested power adjustment values ​​for each wavelength sub-interval in different spatial regions, with clear physical meaning and control direction. For example, in industrial curing applications, if the light intensity of a certain wavelength band is low in a specific area, the corresponding power adjustment parameter will be assigned a positive value to increase the output intensity; conversely, a negative value may be set to avoid overexposure or energy waste. This power modeling method based on singular value decomposition enables the system to extract operable power adjustment strategies from complex multidimensional error data, improving the robustness and adaptability of the entire control system. Next, the system uses the Hamiltonian optimization method to recursively calculate the power adjustment parameter set, generating a power iteration sequence. Hamiltonian optimization is a numerical optimization method based on dynamic systems, whose advantage lies in maintaining high convergence speed and stability during the search process. By iteratively updating the power adjustment parameter set multiple times, the system can gradually approach the optimal power combination that meets the light intensity matching requirements. To ensure the effectiveness of the iteration process, convergence analysis is also required on the generated power iteration sequence to determine whether it tends to a certain stable power state, ultimately obtaining a steady-state power solution set. This solution set represents all possible power configuration results that can reach the convergence state under the current error conditions, forming the basis for subsequent multi-objective optimization. Subsequently, the system performs multi-objective optimization on the steady-state power solution set, aiming to select the optimal solution set that simultaneously satisfies multiple performance indicators from numerous feasible solutions. This typically employs the Pareto Front theory, comprehensively considering multiple optimization objectives such as light intensity matching, energy efficiency, and illumination uniformity, ultimately obtaining the Pareto optimal solution set. Each solution in this set represents a power configuration scheme that achieves a balance among multiple objectives, possessing good practicality and engineering value.Based on this, the system further evaluates the power configuration of the Pareto optimal solution set, combining historical data, empirical models, and practical application requirements to select a set of the most representative candidate power schemes as input for the next simulation step. However, relying solely on light intensity matching cannot fully guarantee the long-term stable operation of the UV LED light source array. Therefore, the system also needs to simulate and model its internal temperature field based on the candidate power schemes to evaluate the impact of power adjustment on the chip junction temperature. The key to this step is to use finite element analysis or heat conduction equations to simulate the temperature distribution of each chip node in the UV LED light source array under the action of candidate power, thereby generating a chip junction temperature distribution map. This map clearly shows which areas have the risk of local overheating and which areas have good heat dissipation under the current power configuration, providing an important basis for subsequent power correction. Finally, the system corrects the candidate power schemes based on the chip junction temperature distribution map to form the final initial power allocation scheme. For example, in a printing ink curing task, if it is found that the chip junction temperature in a certain area is close to the safe threshold due to excessive power, the system will appropriately reduce the power output in that area while ensuring light intensity matching, thereby achieving the best balance between illumination performance and device lifespan. This joint control mechanism based on multi-objective optimization and thermal simulation not only improves the energy efficiency and stability of UV LED light source arrays but also provides strong technical support for intelligent control under complex lighting environments. In summary, this step constructs a complete power solution and optimization process through multiple technical modules, including singular value decomposition to extract light intensity features, constructing a power compensation matrix, Hamiltonian optimization to generate a power iteration sequence, multi-objective optimization to obtain Pareto optimal solutions, temperature field simulation to evaluate chip junction temperature changes, and final power correction. It not only achieves refined power control of the UV LED light source array across multiple wavelength sub-ranges but also lays a solid data foundation and decision support for subsequent optimal power configuration and multi-channel collaborative control. In typical application scenarios such as industrial curing, this power adjustment mechanism based on dynamic iteration and multi-objective optimization can significantly improve the overall illumination accuracy, energy utilization, and operational stability of the system, fully demonstrating its technological advancement and engineering feasibility in the field of high-precision illumination control.

[0102] In a specific embodiment, the step of synchronously controlling each channel of the UV LED light source array according to the optimal power configuration scheme to obtain a control signal sequence for multi-channel coordinated operation includes:

[0103] The optimal power configuration scheme is mapped by pulse frequency to obtain a reference pulse sequence, and the reference pulse sequence is phase-compensated to obtain a channel drive timing table.

[0104] The channel drive timing table is expanded into multiple pulses using time-division multiplexing technology to obtain an initial modulation waveform group. Dead time compensation is then performed based on the initial modulation waveform group to obtain an anti-collision drive sequence.

[0105] The anti-collision drive sequence is subjected to current response characteristic analysis to obtain a channel current prediction spectrum, and waveform shaping is performed based on the channel current prediction spectrum to obtain a modified modulation sequence.

[0106] Based on the modified modulation sequence, channel synchronization control is performed on the UV LED light source array to obtain a control signal sequence for multi-channel collaborative operation.

[0107] Specifically, in the process of "synchronously controlling each channel of the UV LED light source array according to the optimal power configuration scheme to obtain a control signal sequence for multi-channel collaborative operation," this step is a crucial execution link in the entire intelligent control method, transforming theoretical control strategies into actual physical control signals. Its core objective is to transform the optimal power configuration scheme, obtained through multiple rounds of optimization, into a set of control signals capable of precisely driving each light-emitting unit in the UV LED light source array, ensuring high consistency and coordination in terms of time, frequency, and current response, thereby achieving true multi-channel collaborative operation. Specifically, this process first requires pulse frequency mapping of the optimal power configuration scheme, converting the power value corresponding to each wavelength sub-interval into a reference pulse sequence of a specific frequency. This mapping process is typically based on pulse width modulation (PWM) or pulse frequency modulation (PFM) technology. By setting a base clock frequency and adjusting the duty cycle or period length of the pulses according to power requirements, digital pulse signals with different energy output capabilities are generated. Subsequently, the system also needs to perform phase compensation calculations on these reference pulse sequences to eliminate phase offset problems caused by hardware delays, signal propagation path differences, and other factors, ultimately generating a channel drive timing table. This table details when each channel should be turned on or off, along with the corresponding time offset, providing a precise time reference for subsequent multi-channel signal synchronization. Based on this, the system further employs time-division multiplexing technology to perform multi-pulse expansion processing on the signals in the channel drive timing table, expanding the originally independent single pulse into multiple parallel initial modulation waveform groups. This process simulates the waveform superposition effect of multiple channels running simultaneously, enabling the control system to efficiently manage a large number of independent channels with limited hardware resources. However, in actual circuits, due to the turn-on and turn-off time delays of switching devices, if the pulse signals of two adjacent channels switch too closely, it may lead to short circuits or overcurrent. Therefore, the system performs dead-time compensation on the initial modulation waveform groups, inserting a blank time period without output between the switching points of adjacent channels to form an anti-collision drive sequence to avoid safety hazards caused by cross-interference. Next, to further improve control accuracy, the system also needs to analyze the current response characteristics of the anti-collision drive sequence. The core of this step is to simulate and predict the current change trend of the internal LED chip of each channel after the current modulation signal is applied, thereby generating a channel current prediction spectrum. This graph not only reflects the transient current behavior of each channel at different time points, but also reveals its steady-state response characteristics, helping to identify problems such as overshoot, oscillation, or response hysteresis. Based on this predicted graph, the system further shapes the modulation waveform, such as smoothing the rising edge, limiting the peak current, or extending the falling time, thereby generating a corrected modulation sequence.This waveform optimization technique effectively improves system stability and safety, especially under high-power output conditions, significantly reducing the risk of thermal stress damage caused by sudden current changes. Ultimately, the system performs channel synchronization control of the UV LED light source array based on the aforementioned modified modulation sequence, generating a set of control signal sequences for multi-channel collaborative operation. These signals are not only strictly aligned in time but also maintain high consistency in waveform morphology, frequency distribution, and current response, ensuring uniform and stable illumination output across all channels in both spatial and temporal dimensions. For example, in industrial curing applications, when various ink materials require exposure to different wavelengths of ultraviolet light, the system can ensure that multiple UV LED light source channels at wavelengths such as 365nm, 385nm, and 405nm operate precisely at preset power levels and form a consistent illumination field in space, thereby significantly improving curing efficiency and product quality. In summary, this step, through a series of precise control and optimization techniques such as pulse frequency mapping, phase compensation, time-division multiplexing expansion, dead-time compensation, current response modeling, and waveform shaping, constructs a complete multi-channel synchronous control system. It not only achieves seamless integration from optimal power configuration to actual control signals, but also provides a solid guarantee for the efficient, stable, and safe operation of the entire UV LED light source array. In typical industrial curing tasks, this technical solution, based on the combination of dynamic power configuration and multi-channel collaborative control, fully demonstrates its superior performance and broad application prospects in complex lighting environments.

[0108] The multi-channel intelligent control method for UV LED lights in the embodiments of the present invention has been described above. The multi-channel intelligent control system for UV LED lights in the embodiments of the present invention will be described below. Please refer to [link / reference]. Figure 2 One embodiment of the multi-channel intelligent control system for UV LED lights in this invention includes:

[0109] Scanning module 21 is used to perform spectral scanning on the illumination area of ​​the UV LED light source array through a multispectral imaging system to obtain a spectral intensity distribution map;

[0110] The segmentation module 22 is used to divide the UV LED light source array into light wavelength segments based on the spectral intensity distribution map, thereby obtaining multiple wavelength sub-intervals;

[0111] The adjustment module 23 is used to dynamically adjust the power of the UV LED light source array for the multiple wavelength sub-intervals to obtain the optimal power configuration scheme;

[0112] Control module 24 is used to perform synchronous control of each channel of the UV LED light source array according to the optimal power configuration scheme, so as to obtain a control signal sequence for multi-channel coordinated operation;

[0113] The output module 25 is used to adjust the real-time output of the UV LED light source array based on the control signal sequence to obtain a stable multi-channel UV LED light output.

[0114] In this embodiment, the specific implementation of each unit in the above system embodiment is described in the above method embodiment, and will not be repeated here.

[0115] Reference Figure 3 This invention also provides a computer device whose internal structure can be as follows: Figure 3 As shown, the computer device includes a processor, memory, display screen, input device, network interface, and database connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores the operating system, computer programs, and database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores the data corresponding to this embodiment. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements the above-described method.

[0116] Those skilled in the art will understand that Figure 3 The structures shown are merely block diagrams of some structures related to the present invention and do not constitute a limitation on the computer devices on which the present invention is applied.

[0117] An embodiment of the present invention also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method. It is understood that the computer-readable storage medium in this embodiment can be a volatile readable storage medium or a non-volatile readable storage medium.

[0118] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the present invention and embodiments can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual-rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM, etc.

[0119] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, apparatus, article, or method that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, apparatus, article, or method. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, apparatus, article, or method that includes that element.

[0120] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A multi-channel intelligent control method for UV LED lights, characterized in that, When applied to a UV LED light source array, the following steps are included: A spectral intensity distribution map is obtained by scanning the illumination area of ​​the UV LED light source array using a multispectral imaging system. Based on the aforementioned spectral intensity distribution map, the UV LED light source array is divided into light wavelength segments to obtain multiple wavelength sub-intervals; Real-time light intensity monitoring of the UV LED light source array is performed based on the multiple wavelength sub-intervals to obtain the current light intensity data of each sub-interval. The deviation between the current light intensity data and the preset target light intensity distribution is calculated to obtain a light intensity deviation matrix. Singular value decomposition is performed on the light intensity deviation matrix to obtain a light intensity feature vector. A power compensation matrix is ​​constructed based on the light intensity feature vector to obtain a power adjustment parameter set. The power adjustment parameter set is recursively calculated using the Hamiltonian optimization method to obtain a power iteration sequence. Convergence analysis is performed on the power iteration sequence to obtain a steady-state power solution set. Multi-objective optimization is performed on the steady-state power solution set to obtain a Pareto optimal solution set. Power configuration evaluation is performed based on the Pareto optimal solution set to obtain candidate power schemes. Temperature field simulation is performed on the UV LED light source array based on the candidate power schemes to obtain UV... The chip junction temperature distribution map in the LED light source array is obtained, and the power is corrected according to the chip junction temperature distribution map to obtain an initial power allocation scheme. The light intensity distribution is simulated based on the initial power allocation scheme to obtain a simulated light intensity distribution map. The similarity between the simulated light intensity distribution map and the target light intensity distribution is evaluated to obtain an evaluation index value. The initial power allocation scheme is dynamically weighted based on the evaluation index value to obtain the optimal power configuration scheme. The optimal power configuration scheme is mapped by pulse frequency to obtain a reference pulse sequence. Phase compensation is then performed on the reference pulse sequence to obtain a channel drive timing table. The channel drive timing table is then expanded using time-division multiplexing technology to obtain an initial modulation waveform group. Dead-time compensation is performed based on the initial modulation waveform group to obtain an anti-collision drive sequence. Current response characteristics of the anti-collision drive sequence are analyzed to obtain a channel current prediction spectrum. Waveform shaping is then performed based on the channel current prediction spectrum to obtain a corrected modulation sequence. Channel synchronization control of the UV LED light source array is then performed based on the corrected modulation sequence to obtain a control signal sequence for multi-channel collaborative operation. Based on the control signal sequence, the real-time output to the UV LED light source array is adjusted to obtain a stable multi-channel UV LED light output.

2. The multi-channel intelligent control method for UV LED lights according to claim 1, characterized in that, The step of performing a spectral scan of the illuminated area of ​​the UV LED light source array using a multispectral imaging system to obtain a spectral intensity distribution map includes: The wavelength of the illumination area of ​​the UV LED light source array is scanned by a multispectral imaging system to obtain the original spectral data, and the original spectral data is subjected to Fourier transform to obtain frequency domain spectral information. The frequency domain spectral information is subjected to adaptive denoising processing to obtain filtered spectral data, and the filtered spectral data is spatially interpolated to obtain density spectral sampling points. The density spectral sampling points are subjected to curve fitting to obtain a continuous spectral curve. Based on the continuous spectral curve, the spectral intensity distribution of the UV LED light source array is mapped in two dimensions to obtain a spectral intensity distribution map.

3. The multi-channel intelligent control method for UV LED lights according to claim 1, characterized in that, Based on the spectral intensity distribution map, the UV LED light source array is divided into light wavelength segments to obtain multiple wavelength sub-intervals, including: Peak features are extracted from the spectral intensity distribution map to obtain a spectral peak sequence, and valley analysis is performed on the spectral peak sequence to obtain wavelength boundary points; The spectral intensity distribution map is dynamically segmented based on the wavelength boundary point to obtain an initial band interval, and the spectral energy density of the initial band interval is calculated to obtain a band energy distribution map. Cluster analysis is performed on the energy distribution map of the band to obtain the band clustering results, and boundary optimization is performed based on the band clustering results to obtain the optimized wavelength segmentation threshold; The UV LED light source array is divided into wavelength sub-intervals based on the optimized wavelength segmentation threshold.

4. The multi-channel intelligent control method for UV LED lights according to claim 3, characterized in that, The dynamic segmentation of the spectral intensity distribution map based on the wavelength boundary point to obtain the initial band interval includes: Interpolation fitting is performed on the wavelength boundary point to obtain the wavelength boundary function, and the wavelength boundary function is differentiated to obtain the wavelength gradient curve; Based on the wavelength gradient curve, a dynamic threshold is set on the spectral intensity distribution map to obtain a band segmentation baseline. Based on the band segmentation baseline, the spectral intensity distribution map is divided into regions to obtain preliminary band segmentation results. The system detects whether there are overlapping regions in the preliminary band division results. If there are, an overlapping band set is obtained, and the band division baseline is locally adjusted based on the overlapping band set to obtain a corrected band division baseline. The spectral intensity distribution map is finally segmented based on the corrected band segmentation baseline to obtain the initial band interval.

5. A multi-channel intelligent control system for UV LED lights, characterized in that, Applications include UV LED light source arrays, including: The scanning module is used to perform spectral scanning on the illuminated area of ​​the UV LED light source array using a multispectral imaging system to obtain a spectral intensity distribution map; The segmentation module is used to divide the UV LED light source array into light wavelength segments based on the spectral intensity distribution map, thereby obtaining multiple wavelength sub-intervals; The adjustment module is used to monitor the light intensity of the UV LED light source array in real time based on the multiple wavelength sub-intervals, obtain the current light intensity data of each sub-interval, and calculate the deviation between the current light intensity data and the preset target light intensity distribution to obtain a light intensity deviation matrix; perform singular value decomposition on the light intensity deviation matrix to obtain a light intensity feature vector, and construct a power compensation matrix based on the light intensity feature vector to obtain a power adjustment parameter set; recursively calculate the power adjustment parameter set using the Hamiltonian optimization method to obtain a power iteration sequence, and perform convergence analysis on the power iteration sequence to obtain a steady-state power solution set; perform multi-objective optimization on the steady-state power solution set to obtain a Pareto optimal solution set, and perform power configuration evaluation based on the Pareto optimal solution set to obtain a candidate power scheme; and perform temperature field simulation on the UV LED light source array based on the candidate power scheme to obtain a UV... The chip junction temperature distribution map in the LED light source array is obtained, and the power is corrected according to the chip junction temperature distribution map to obtain an initial power allocation scheme. The light intensity distribution is simulated based on the initial power allocation scheme to obtain a simulated light intensity distribution map. The similarity between the simulated light intensity distribution map and the target light intensity distribution is evaluated to obtain an evaluation index value. The initial power allocation scheme is dynamically weighted based on the evaluation index value to obtain the optimal power configuration scheme. The control module is used to perform pulse frequency mapping on the optimal power configuration scheme to obtain a reference pulse sequence, and to perform phase compensation calculation on the reference pulse sequence to obtain a channel drive timing table; to perform multi-path pulse expansion on the channel drive timing table using time-division multiplexing technology to obtain an initial modulation waveform group, and to perform dead-time compensation based on the initial modulation waveform group to obtain an anti-collision drive sequence; to perform current response characteristic analysis on the anti-collision drive sequence to obtain a channel current prediction spectrum, and to perform waveform shaping processing based on the channel current prediction spectrum to obtain a corrected modulation sequence; and to perform channel synchronization control on the UV LED light source array based on the corrected modulation sequence to obtain a control signal sequence for multi-channel collaborative operation. The output module is used to adjust the real-time output of the UV LED light source array based on the control signal sequence to obtain a stable multi-channel UV LED light output.

6. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method according to any one of claims 1 to 4.

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