Dynamic frequency adjustment method and system of intelligent self-adaptive UV variable-frequency power supply

Abstract

The invention relates to the field of UV power supply frequency adjustment, in particular to a dynamic frequency adjustment method and system for an intelligent self-adaptive UV variable-frequency power supply. The method comprises the following steps: acquiring environment illumination parameters of a plurality of orientations, an environment image of a to-be-processed target and a preset UV radiation demand log; performing illumination dynamic rendering on the environment image of the to-be-processed target based on the environment illumination parameters of the plurality of orientations to generate a scene light intensity distribution diagram; real-time operation multi-point sampling is carried out on the UV variable-frequency power supply, so that a power supply frequency fluctuation curve is constructed; performing three-dimensional point cloud modeling on the scene light intensity distribution diagram according to the power supply frequency fluctuation curve, performing dynamic light simulation mapping, and constructing a dynamic power supply twinborn model; performing multi-frequency to-be-processed target irradiation simulation on the dynamic power supply twinning model based on the simulation time axis so as to generate time sequence radiation response change data of the target; according to the invention, stable and self-adaptive UV variable-frequency power supply frequency adjustment according to the environment is realized.

Description

Technical Field

[0001] This invention relates to the field of UV power supply frequency adjustment, and more particularly to a method and system for dynamic frequency adjustment of an intelligent adaptive UV inverter power supply. Background Technology

[0002] With the continuous improvement of industrial automation, UV inverter power supplies are widely used in various industrial production and civilian fields, becoming a key support for the stable operation of automation systems. As the core power supply component for UV light sources, UV inverter power supplies need to undertake the important task of adjusting and controlling the intensity of UV light over a long period of time.

[0003] In practical applications, the output frequency of UV inverter power supplies is affected by various internal and external factors, such as temperature changes, aging, and load fluctuations, which cause dynamic changes in UV light intensity. These frequency fluctuations can lead to unstable UV irradiation effects, affecting the reliability and consistency of UV applications. Traditional UV inverter power supplies mostly use static frequency setting methods, which cannot sense and automatically adjust the frequency in real time, making it difficult to adapt to complex and ever-changing working environments.

[0004] Therefore, there is an urgent need to develop an intelligent adaptive UV inverter power supply dynamic frequency adjustment technology that can monitor and analyze the working status of the UV light source in real time, and maintain the UV light intensity at its optimal state through automated frequency adjustment, thereby ensuring the stability and reliability of UV applications. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention proposes a dynamic frequency adjustment method and system for an intelligent adaptive UV inverter power supply, thereby resolving at least one of the aforementioned technical problems.

[0006] To achieve the above objectives, the present invention provides a dynamic frequency adjustment method for an intelligent adaptive UV inverter power supply, comprising the following steps: Step S1: Obtain ambient lighting parameters from multiple directions, the environmental image of the target to be processed, and the preset UV radiation requirement log; perform dynamic lighting rendering on the environmental image of the target to be processed based on the ambient lighting parameters from multiple directions to generate a scene light intensity distribution map. Step S2: Perform real-time multi-point sampling of the UV inverter power supply and fit the power supply frequency fluctuation to construct the power supply frequency fluctuation curve. Step S3: Based on the power frequency fluctuation curve, perform 3D point cloud modeling of the scene light intensity distribution map, and perform dynamic light simulation mapping to construct a dynamic power twin model; Step S4: Define the simulation time axis, perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis, and perform time-series radiation response change analysis to generate time-series radiation response change data of the target; Step S5: Based on the preset UV radiation demand log, predict the radiation demand deviation of the target’s time-series radiation response change data, and perform radiation deviation compensation calculation to obtain the UV radiation deviation compensation value. Step S6: Calculate the optimal adjustment frequency for the dynamic power supply twin model based on the UV radiation deviation compensation value, and perform adaptive dynamic frequency modulation optimization to construct an adaptive dynamic frequency modulation model.

[0007] This invention utilizes dynamic lighting rendering based on ambient lighting parameters from multiple directions to generate a scene light intensity distribution map. This simulates real-world scenes under different lighting conditions, providing a detailed understanding of the ambient lighting conditions of the target object and laying the foundation for subsequent power frequency adjustments. Through real-time multi-point sampling and fluctuation fitting, a power frequency fluctuation curve is constructed to understand the frequency changes of the UV inverter power supply. This fluctuation curve provides an assessment of power frequency stability, laying the basis for subsequent frequency tuning optimization. Based on the power frequency fluctuation curve, a 3D point cloud model is created from the light intensity distribution map to achieve dynamic light simulation mapping, constructing a dynamic power supply twin model. This dynamic power supply twin model combines lighting conditions and power frequency fluctuations, providing a more realistic simulation scene. Further frequency tuning optimization is then performed based on the simulation time axis. Multi-frequency irradiation simulations were conducted to analyze the temporal radiation response changes of the target, understand the impact of UV radiation on the target at different frequencies, and generate temporal radiation response change data to predict the target's radiation response at different frequencies, providing guidance for subsequent frequency modulation optimization. Radiation demand deviation was predicted from the temporal radiation response data, and radiation deviation compensation values ​​were calculated to achieve precise control of UV radiation. The radiation deviation compensation values ​​provided a reference for correcting UV radiation, optimizing the adjustment strategy of the dynamic frequency modulation model. Based on the UV radiation deviation compensation values, the optimal adjustment frequency was calculated, and an adaptive dynamic frequency modulation model was constructed to achieve adaptive frequency modulation of the UV inverter power supply. The adaptive dynamic frequency modulation model combined real-time illumination conditions and power supply frequency fluctuations, providing a stable and intelligent frequency modulation scheme to ensure that the target's UV radiation requirements are met.

[0008] Preferably, step S1 includes the following steps: Step S11: Acquire ambient lighting parameters from multiple directions, environmental images of the target to be processed, and preset UV radiation demand logs based on multiple sensors; Step S12: Calculate the ambient light intensity for multiple directions to obtain ambient light intensity data for multiple directions; Step S13: Perform spatial distribution analysis of ambient light intensity data from multiple directions to generate spatial distribution data of ambient light intensity; Step S14: Perform scene visual feature analysis on the environmental image of the target to be processed, and extract the current scene visual feature data; Step S15: Perform dynamic lighting rendering on the current scene visual feature data based on the ambient light intensity spatial distribution data to generate a scene light intensity distribution map.

[0009] This invention acquires environmental data through multiple sensors, including ambient light parameters from multiple directions, environmental images of the target object, and UV radiation demand logs, providing a comprehensive data foundation. The integrated utilization of multi-sensor data improves data accuracy and reliability, providing a more reliable basis for subsequent analysis and adjustments. Ambient light parameters from multiple directions are calculated to obtain ambient light intensity data from various locations, providing foundational data for subsequent analysis. The obtained light intensity data is used to compare lighting conditions from different directions, providing a reference for dynamic lighting rendering. Analysis based on the ambient light intensity data from multiple directions generates spatial distribution data of ambient light intensity, aiding in the understanding of… The system analyzes the spatial distribution of light intensity, optimizes lighting conditions using spatial distribution data, and improves the uniformity and adaptability of lighting. It performs scene visual feature analysis on the environmental image of the target object, extracting visual feature data of the current scene to understand the target scene. The extracted visual feature data helps to understand the characteristics and requirements of the scene, providing a basis for subsequent dynamic lighting rendering. Based on the spatial distribution data of ambient light intensity, it performs dynamic lighting rendering on the visual feature data of the current scene, generating a scene light intensity distribution map to display the lighting conditions. The scene light intensity distribution map shows the distribution of light in the scene, providing a visual reference for the dynamic frequency adjustment of the intelligent adaptive UV inverter power supply.

[0010] Preferably, step S14 specifically includes the following steps: Calculate the spatial coordinates of each sensor; Based on the spatial location coordinates, multiple directional ambient light intensity data are matched to obtain ambient light intensity location matching data; Based on the location matching data of ambient light intensity, spatial distribution analysis of light intensity is performed to generate spatial distribution data of ambient light intensity.

[0011] This invention ensures the spatial accuracy of environmental data acquisition and analysis by calculating the spatial coordinates of each sensor. These spatial coordinates correlate sensor-collected data with specific locations, providing location information for subsequent data analysis. Based on these sensor coordinates, ambient light intensity data from multiple directions are matched with specific sensor locations to form ambient light intensity location-matched data. This matched data is more practical, visually displaying the lighting conditions at different locations and providing a foundation for spatial distribution analysis of light. Analysis based on this location-matched data generates ambient light intensity spatial distribution data, showcasing the spatial distribution of light. This spatial distribution data optimizes lighting conditions, improving uniformity and adaptability, and providing a basis for dynamic frequency adjustment of intelligent adaptive UV inverter power supplies.

[0012] Preferably, step S2 specifically includes the following steps: Step S21: Perform real-time multi-point sampling of the UV inverter power supply and extract the power supply operating status parameters at multiple time points; Step S22: Perform a short-time Fourier transform on the power supply operating state parameters at multiple time points to obtain multiple time window frequency domain data; Step S23: Perform spectral distribution analysis on the frequency domain data of multiple time windows to generate spectral distribution data for each time window; Step S24: Perform dynamic time-frequency variation analysis on the spectral distribution data of each time window to generate dynamic time-frequency variation data of the power supply; Step S25: Perform real-time power frequency fluctuation fitting on the power supply dynamic time-frequency change data to construct the power supply frequency fluctuation curve.

[0013] This invention obtains real-time data on the operating status of a UV inverter power supply by performing real-time multi-point sampling and extracting power supply operating status parameters at multiple time points. This extraction of operating status parameters at multiple time points reveals the power supply's operating characteristics and trends, providing a data foundation for subsequent analysis. Short-time Fourier transforms are performed on the operating status parameters at multiple time points to obtain frequency domain data within multiple time windows, revealing the characteristics of the power signal in both the time and frequency domains. This time-frequency domain data provides a basis for subsequent spectral distribution analysis, helping to understand the spectral characteristics of the power signal. Spectral distribution analysis is then performed on the frequency domain data from multiple time windows to generate spectral distribution data for each time window, displaying the power signal's spectrum within different time windows. The system uses spectral distribution data to observe the spectral changes of the power signal, providing a basis for dynamic time-frequency variation analysis. It performs dynamic time-frequency variation analysis on the spectral distribution data for each time window, generating dynamic time-frequency variation data for the power supply. This data displays the time-frequency changes of the power signal within different time windows, observes the time-frequency variation trend of the power signal, and provides data support for subsequent power frequency fluctuation fitting. Real-time power frequency fluctuation fitting is performed on the dynamic time-frequency variation data to construct a power frequency fluctuation curve, showing the fluctuation of the power frequency at different time points. The power frequency fluctuation curve analyzes the fluctuation characteristics of the power frequency, providing a basis for the dynamic frequency adjustment of the intelligent adaptive UV inverter power supply.

[0014] Preferably, step S3 specifically includes the following steps: Step S31: Perform scene 3D structure analysis on the environmental image of the target to be processed, and generate scene 3D structure data of the target to be processed; Step S32: Based on the 3D structural data of the scene of the target to be processed, perform 3D point cloud modeling on the scene light intensity distribution map to construct a 3D scene model; Step S33: Perform multi-frequency power supply light propagation simulation on the UV inverter power supply based on the power supply frequency fluctuation curve to obtain light propagation simulation data for multiple frequencies; Step S34: Use light propagation simulation data of multiple frequencies to perform dynamic light simulation mapping on the 3D scene model and construct a dynamic power twin model.

[0015] This invention, through 3D scene structure analysis, accurately captures the 3D structure of the environment, including object positions and spatial relationships, providing detailed scene information for subsequent light propagation simulation. Understanding the 3D structure of the scene optimizes lighting adjustment, ensuring that light propagates correctly to the target area and improving energy efficiency. By using stereo point cloud modeling, a high-precision 3D scene model is constructed, more realistically simulating the propagation and reflection of light in the scene, providing more accurate data support for power frequency adjustment. Based on the 3D scene model, the propagation of light in the environment is monitored in real time, allowing for timely frequency adjustment to optimize lighting effects. Through multi-frequency light propagation simulation, the propagation characteristics of light at different frequencies are understood, providing a basis for selecting the optimal frequency adjustment scheme. By simulating light propagation, lighting effects are optimized, thereby reducing unnecessary energy consumption and achieving energy conservation and emission reduction. A dynamic power supply twin model is constructed, enabling real-time mapping of the impact of power frequency adjustment on ambient lighting, providing real-time feedback and adjustment capabilities. Through dynamic light simulation mapping, intelligent adaptive adjustments are made based on scene changes and power frequency fluctuations, improving intelligence and efficiency.

[0016] Preferably, the specific steps of step S4 are as follows: Step S41: Define multiple time period windows and set different UV power supply frequencies for each time period; Step S42: Perform time-series window sequence fitting on multiple time-segment windows to construct a simulated time axis; Step S43: Perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis to obtain target illumination simulation response data; Step S44: Calculate the UV radiation intensity over multiple time periods from the target irradiation simulation response data to obtain multiple UV radiation intensities; Step S45: Perform target radiation response trend analysis on the target irradiation simulation response data to generate radiation response trend data for multiple time periods; Step S46: Perform time-series radiation response change analysis on radiation response trend data for multiple time periods based on multiple UV radiation intensities, thereby generating time-series radiation response change data of the target.

[0017] This invention sets different UV power supply frequencies for each time period window, enabling personalized frequency adjustments based on specific needs and environmental changes. The frequency settings for different time periods allow for flexible responses to varying light requirements within different time periods, improving adaptability. By sequentially fitting time-series windows, a simulated time axis is constructed, organizing and understanding the sequential arrangement of UV power supply frequencies within different time periods, providing temporal support for subsequent simulations. Based on the simulated time axis, a multi-frequency target irradiation simulation is performed on a dynamic power supply twin model to obtain target irradiation simulation response data, helping to understand the target irradiation effect at different frequencies. Through simulated irradiation data, the UV power supply frequency is precisely adjusted to achieve a more suitable irradiation effect. Multiple time periods of UV radiation intensity are calculated from the target irradiation simulation response data, obtaining multiple UV radiation intensity data to understand the changing trends of UV radiation over different time periods. By analyzing the target irradiation simulation response data, radiation response trend data for multiple time periods is generated, helping to predict the trend changes in target radiation response and providing a reference for further frequency adjustments. Based on the UV radiation intensity and radiation response trend data, a time-series radiation response change analysis is performed to generate time-series radiation response change data for the target, understanding the changing impact of UV radiation on the target.

[0018] Preferably, the specific steps of step S5 are as follows: Step S51: Evaluate the UV radiation effect of the target's time-series radiation response change data in multiple stages to obtain the UV radiation effect in multiple stages; Step S52: Perform multi-stage preset demand analysis on the preset UV radiation demand log and extract the radiation demand data for each stage. Step S53: Based on the radiation demand data of each stage, perform radiation demand deviation prediction on the UV radiation effect of multiple stages to obtain multi-stage UV radiation demand deviation prediction data. Step S54: Perform radiation deviation compensation calculation on the multi-stage UV radiation demand deviation prediction data to obtain the UV radiation deviation compensation value.

[0019] This invention assesses the effects of UV radiation in multiple stages. The system analyzes the temporal radiation response changes of the target to obtain the UV radiation effect at different stages, understands the changing trends of UV radiation over different time periods, and quantifies the radiation situation at different time periods by evaluating the UV radiation effect at multiple stages. This provides a reference for subsequent demand analysis and compensation calculations. By analyzing a preset UV radiation demand log, the system can extract radiation demand data for each stage, understand the changes in user demand for UV radiation, and analyze and predict the changing trends of UV radiation demand for each stage, providing a basis for subsequent compensation calculations. By predicting the deviation of the multi-stage UV radiation effect based on the radiation demand data, the system can analyze the deviation between the UV radiation effect at each stage and the actual demand, helping to identify potential problems and providing optimization and adjustment suggestions to ensure that the system meets the user's radiation needs at different stages. By analyzing the predicted deviation data of multi-stage UV radiation demand, the system calculates the UV radiation deviation compensation value, which is used to adjust the output frequency of the UV inverter power supply to achieve dynamic frequency adjustment. The UV radiation deviation compensation calculation system accurately adjusts the frequency according to the predicted demand deviation, improving the stability and quality of the UV radiation effect.

[0020] Preferably, the specific steps of step S54 are as follows: A stage deviation distribution analysis was performed on the multi-stage UV radiation demand deviation prediction data to generate stage deviation distribution data. The time-series distribution pattern evolution of the stage deviation distribution data is performed to obtain the radiation deviation distribution pattern data; Time-series deviation fitting is performed on the radiation deviation distribution data to construct the radiation deviation distribution curve; Deeply analyze the changes in the radiation deviation distribution curve to identify the patterns of radiation deviation. Extract the radiation demand deviation value for each stage of the multi-stage UV radiation demand deviation prediction data; The average radiation demand deviation is calculated by averaging all the radiation demand deviation values ​​to generate the average radiation demand deviation. Based on the radiation deviation law, the average value of radiation demand deviation is calculated for full-stage radiation deviation compensation, thereby obtaining the UV radiation deviation compensation value.

[0021] This invention analyzes the stage deviation distribution of multi-stage UV radiation demand deviation prediction data to generate stage deviation distribution data, providing an intuitive understanding of the deviation situation at different stages and laying the foundation for subsequent analysis. By performing time-series distribution trend evolution analysis on the stage deviation distribution data, the changing trend of radiation deviation is monitored in real time, helping to identify potential problems early and take corresponding adjustment measures. By fitting the radiation deviation distribution data to the time-series deviation, a radiation deviation distribution curve is constructed, accurately describing the changing trend of the deviation and laying the foundation for subsequent analysis. In-depth change mining of the radiation deviation distribution curve identifies the potential patterns of radiation deviation, providing theoretical support for subsequent frequency adjustment. The radiation demand deviation value of each stage of the multi-stage UV radiation demand deviation prediction data is extracted and averaged to help integrate the demand deviations of each stage, providing basic data for subsequent compensation calculations. Based on the radiation deviation patterns, full-stage radiation deviation compensation calculations are performed, and the dynamic frequency of the UV inverter power supply is intelligently adjusted according to the actual situation to achieve more stable and accurate radiation demand, improving the system's adaptability and performance.

[0022] Preferably, the specific steps of step S6 are as follows: Step S61: Based on the UV radiation deviation compensation value, perform frequency iterative adjustment simulation on the dynamic power supply twin model to obtain multiple frequency adjustment strategies; Step S62: Quantize the deviation compensation for multiple frequency adjustment strategies to obtain the compensation effect of each strategy; Step S63: Calculate the optimal adjustment frequency based on the compensation effect of each strategy to obtain the optimal light source adjustment frequency; Step S64: Based on the optimal light source adjustment frequency, perform adaptive dynamic frequency adjustment optimization on the UV inverter power supply, construct an adaptive dynamic frequency adjustment model, and execute the dynamic frequency adjustment operation of the UV inverter power supply.

[0023] This invention uses a twin model to simulate iterative frequency adjustment, simulating multiple frequency adjustment strategies to provide diverse adjustment schemes. It generates multiple frequency adjustment strategies to explore the impact of different frequency modulation methods on UV radiation effects, providing more options for subsequent optimization. The invention quantifies the deviation compensation effect of multiple strategies to evaluate the effectiveness of each frequency adjustment strategy in correcting UV radiation deviations. By quantifying the compensation effect, it compares the actual impact of different strategies, providing a basis for selecting the optimal frequency modulation strategy. By calculating the compensation effect of each strategy, the optimal adjustment frequency is determined to maximize the quality and stability of UV radiation effects. Determining the optimal light source adjustment frequency allows for more precise adjustment of the UV inverter power supply frequency, improving the adaptability of UV radiation effects. Based on the optimal light source adjustment frequency, dynamic frequency modulation optimization achieves adaptability. The frequency of the UV inverter power supply is adjusted according to real-time requirements. The constructed adaptive dynamic frequency modulation model enables the UV inverter power supply to dynamically adjust its frequency according to different situations, ensuring continuous and stable UV radiation effects. This specification provides a dynamic frequency adjustment system for an intelligent adaptive UV inverter power supply, used to execute the dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply as described above, including: The dynamic lighting rendering module is used to acquire ambient lighting parameters from multiple directions, the environmental image of the target to be processed, and the preset UV radiation requirement log; based on the ambient lighting parameters from multiple directions, it performs dynamic lighting rendering on the environmental image of the target to be processed to generate a scene light intensity distribution map. The frequency fluctuation module is used to perform real-time multi-point sampling of the UV inverter power supply and fit the power supply frequency fluctuation to construct the power supply frequency fluctuation curve. The dynamic ray simulation module is used to perform 3D point cloud modeling of scene light intensity distribution map based on power frequency fluctuation curve, and to perform dynamic ray simulation mapping to build dynamic power supply twin model. The radiation response change module is used to define the simulation time axis, perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis, and perform time-series radiation response change analysis to generate time-series radiation response change data of the target. The deviation prediction module is used to predict the radiation demand deviation of the target based on the preset UV radiation demand log and to perform radiation deviation compensation calculation to obtain the UV radiation deviation compensation value. The adaptive dynamic frequency modulation module is used to calculate the optimal adjustment frequency of the dynamic power supply twin model based on the UV radiation deviation compensation value, and to perform adaptive dynamic frequency modulation optimization to build an adaptive dynamic frequency modulation model.

[0024] This invention acquires ambient lighting parameters from multiple directions, environmental images of the target object, and UV radiation demand logs to provide necessary input information for subsequent processing. Based on the ambient lighting parameters, it performs dynamic lighting rendering on the target's environmental image, generating a scene light intensity distribution map to simulate a realistic lighting environment. Real-time multi-point sampling of the UV inverter power supply is performed, and frequency fluctuation fitting is conducted to construct a power supply frequency fluctuation curve. This allows the system to more accurately understand the power supply frequency fluctuations. By constructing the power supply frequency fluctuation curve, the system can better control the stability of the power supply frequency and improve the consistency of UV radiation. Based on the power supply frequency fluctuation curve, a 3D point cloud model is created, and dynamic light simulation mapping is performed to construct a dynamic power supply twin model, improving the accuracy and realism of the lighting simulation. Dynamic light simulation mapping enables the system to better simulate light changes at different frequencies, optimizing the performance of UV radiation effects. A simulation time axis is defined, and the dynamic power supply twin model is subjected to multi-frequency... Target irradiation simulation is performed to analyze the time-series radiation response changes, generating time-series radiation response change data. This allows the system to gain a more comprehensive understanding of the changes in UV radiation effects. The analysis of time-series radiation response change data helps the system assess the impact of UV radiation on the target at different frequencies, providing a basis for subsequent frequency adjustment. Based on the UV radiation demand log, deviation prediction is performed on the time-series radiation response change data, and radiation deviation compensation calculations are performed to obtain UV radiation deviation compensation values. This allows the system to respond more accurately to changes in UV radiation demand. Radiation deviation compensation calculations optimize the UV radiation effect, enabling the system to better adapt to different demand scenarios. Based on the UV radiation deviation compensation values, the optimal adjustment frequency is calculated for the dynamic power supply twin model, performing adaptive dynamic frequency adjustment optimization and constructing an adaptive dynamic frequency adjustment model. The system intelligently adjusts the frequency to meet UV radiation demands. The adaptive dynamic frequency adjustment module optimizes the frequency adjustment of the UV inverter power supply, improving the system's energy efficiency and the stability of UV radiation effects. Attached Figure Description

[0025] Figure 1 This is a flowchart illustrating the steps of a dynamic frequency adjustment method for an intelligent adaptive UV inverter power supply according to the present invention. Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1. Figure 3 This is a detailed flowchart illustrating the implementation steps of step S2; Figure 4 This is a flowchart illustrating the detailed implementation steps of step S3. Detailed Implementation

[0026] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0027] This application provides a method and system for dynamic frequency adjustment of an intelligent adaptive UV inverter power supply. The executing entities of the method and system include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, and network upload devices that can be considered general computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0028] Please see Figures 1 to 4 This invention provides a dynamic frequency adjustment method for an intelligent adaptive UV inverter power supply, which includes the following steps: Step S1: Obtain ambient lighting parameters from multiple directions, the environmental image of the target to be processed, and the preset UV radiation requirement log; perform dynamic lighting rendering on the environmental image of the target to be processed based on the ambient lighting parameters from multiple directions to generate a scene light intensity distribution map. Step S2: Perform real-time multi-point sampling of the UV inverter power supply and fit the power supply frequency fluctuation to construct the power supply frequency fluctuation curve. Step S3: Based on the power frequency fluctuation curve, perform 3D point cloud modeling of the scene light intensity distribution map, and perform dynamic light simulation mapping to construct a dynamic power twin model; Step S4: Define the simulation time axis, perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis, and perform time-series radiation response change analysis to generate time-series radiation response change data of the target; Step S5: Based on the preset UV radiation demand log, predict the radiation demand deviation of the target’s time-series radiation response change data, and perform radiation deviation compensation calculation to obtain the UV radiation deviation compensation value. Step S6: Calculate the optimal adjustment frequency for the dynamic power supply twin model based on the UV radiation deviation compensation value, and perform adaptive dynamic frequency modulation optimization to construct an adaptive dynamic frequency modulation model.

[0029] This invention utilizes dynamic lighting rendering based on ambient lighting parameters from multiple directions to generate a scene light intensity distribution map. This simulates real-world scenes under different lighting conditions, providing a detailed understanding of the ambient lighting conditions of the target object and laying the foundation for subsequent power frequency adjustments. Through real-time multi-point sampling and fluctuation fitting, a power frequency fluctuation curve is constructed to understand the frequency changes of the UV inverter power supply. This fluctuation curve provides an assessment of power frequency stability, laying the basis for subsequent frequency tuning optimization. Based on the power frequency fluctuation curve, a 3D point cloud model is created from the light intensity distribution map to achieve dynamic light simulation mapping, constructing a dynamic power supply twin model. This dynamic power supply twin model combines lighting conditions and power frequency fluctuations, providing a more realistic simulation scene. Further frequency tuning optimization is then performed based on the simulation time axis. Multi-frequency irradiation simulations were conducted to analyze the temporal radiation response changes of the target, understand the impact of UV radiation on the target at different frequencies, and generate temporal radiation response change data to predict the target's radiation response at different frequencies, providing guidance for subsequent frequency modulation optimization. Radiation demand deviation was predicted from the temporal radiation response data, and radiation deviation compensation values ​​were calculated to achieve precise control of UV radiation. The radiation deviation compensation values ​​provided a reference for correcting UV radiation, optimizing the adjustment strategy of the dynamic frequency modulation model. Based on the UV radiation deviation compensation values, the optimal adjustment frequency was calculated, and an adaptive dynamic frequency modulation model was constructed to achieve adaptive frequency modulation of the UV inverter power supply. The adaptive dynamic frequency modulation model combined real-time illumination conditions and power supply frequency fluctuations, providing a stable and intelligent frequency modulation scheme to ensure that the target's UV radiation requirements are met.

[0030] In the embodiments of the present invention, see Figure 1 This is a flowchart illustrating the steps of a dynamic frequency adjustment method for an intelligent adaptive UV inverter power supply according to the present invention. In this example, the steps of the dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply include: Step S1: Obtain ambient lighting parameters from multiple directions, the environmental image of the target to be processed, and the preset UV radiation requirement log; perform dynamic lighting rendering on the environmental image of the target to be processed based on the ambient lighting parameters from multiple directions to generate a scene light intensity distribution map. In this embodiment, ambient light parameters are measured at multiple locations (e.g., different angles and heights) using light sensors (such as illuminance meters and spectrophotometers). The light intensity (e.g., illuminance, in lx), light source type (e.g., sunlight, artificial light), and spectral distribution data at different locations are recorded. The acquired light parameters are organized into a data frame, including information such as light intensity, azimuth angle, and height, ensuring a clear data structure. High-resolution cameras are used to capture environmental images of the target from multiple angles, ensuring high image quality and sufficient detail. During image acquisition, environmental conditions are kept as consistent as possible to avoid the influence of changes in natural light. The captured environmental images are stored by angle or time markers to form an image dataset, facilitating subsequent processing. A UV radiation demand log related to the target is collected, recording UV radiation parameters (e.g., intensity, wavelength, irradiation time) at each stage, ensuring that the demand information contained in the log is consistent with... The characteristics of the target to be processed are consistent to facilitate subsequent radiation effect analysis. The UV radiation demand log is organized into a data frame, ensuring that it includes the demand values, duration, and other relevant parameters for each stage. A suitable graphics rendering software or engine (such as the rendering tools of Blender, Unity, or MATLAB) is selected to ensure that it supports dynamic lighting rendering. The environmental image of the target to be processed is imported into the rendering software, and the scene light source is set according to the obtained lighting parameters. The position, intensity, color, and type of the light source (such as point light source, parallel light source, etc.) are set, and dynamic adjustments are made according to the lighting parameters. The dynamic lighting rendering process is run to generate a scene light intensity distribution map. Ray tracing technology is used to simulate the propagation of light in the scene, calculate the impact of lighting on the target to be processed, and generate a light intensity distribution map (such as a heat map or intensity map). The generated scene light intensity distribution map is saved in an appropriate format (such as PNG, JPEG) and stored in a data frame for subsequent analysis.

[0031] Step S2: Perform real-time multi-point sampling of the UV inverter power supply and fit the power supply frequency fluctuation to construct the power supply frequency fluctuation curve. In this embodiment, a UV inverter power supply is configured to ensure it can operate at different frequencies and connected to a data acquisition system (such as a data acquisition card or oscilloscope) to determine the sampling frequency and sampling time, ensuring that real-time data of frequency fluctuations can be captured. Generally, the sampling frequency should be higher than twice the signal frequency. During the operation of the UV inverter power supply, frequency data from multiple points are collected in real time through an automated data acquisition system, ensuring that the timestamp and frequency value of each sampling point are recorded. The sampled frequency data is organized into a data frame, ensuring that it includes the frequency value and corresponding timestamp of each time point. The sampled data is checked for outliers or noise, and a suitable filter (such as a low-pass filter) is used to remove high-frequency noise. Mobile applications are then applied. Averages or other smoothing techniques are used to process frequency data to reduce the impact of random fluctuations and make the data more readable. Statistical analysis software (such as Python's NumPy and SciPy libraries, MATLAB, etc.) is used to fit the processed frequency data to obtain frequency fluctuation curves. Least squares are used for fitting to determine model parameters. The fitting results (such as the fitted curve, model parameters, and goodness of fit) are recorded in a data frame, and visualization charts (such as line charts or scatter plots) are generated to display the frequency fluctuation curves. The fitted frequency fluctuation curves are analyzed to identify the frequency, amplitude, and periodic characteristics of the fluctuations. A report is generated, including charts of frequency fluctuations, model parameters, and their physical meanings, to facilitate subsequent analysis and decision-making.

[0032] Step S3: Based on the power frequency fluctuation curve, perform 3D point cloud modeling of the scene light intensity distribution map, and perform dynamic light simulation mapping to construct a dynamic power twin model; In this embodiment, a computer vision library (such as OpenCV or PIL) is used to read the light intensity distribution map and extract the light intensity values. Stereo point cloud data is then generated based on these light intensity values. Pixel brightness is used as the Z-axis coordinate, and the X and Y axes correspond to the image coordinates.

[0033] import numpy as np import cv2 # Read the light intensity distribution map img = cv2.imread('light_distribution.png', cv2.IMREAD_GRAYSCALE) height, width = img.shape # Generating point clouds x, y = np.meshgrid(np.arange(width), np.arange(height)) z = img.flatten() # The light intensity value is used as the Z-axis point_cloud = np.column_stack((x.flatten(), y.flatten(), z)) Choose a suitable 3D graphics engine or library, such as Blender, Unity, or Unreal Engine, and ensure that it supports dynamic lighting simulation.

[0034] 3D scene construction: Import the generated point cloud data into the selected 3D environment and build the corresponding 3D scene.

[0035] In Blender, a Python script is used to import point clouds and generate a mesh.

[0036] import bpy import bmesh # Clear scene bpy.ops.object.select_all(action='DESELECT') bpy.ops.object.select_all(action='SELECT') bpy.ops.object.delete() # Creating a point cloud mesh = bpy.data.meshes.new("PointCloud") obj = bpy.data.objects.new("PointCloud", mesh) bpy.context.collection.objects.link(obj) # Add Vertices bm = bmesh.new() for point in point_cloud: bmesh.ops.create_vert(bm, co=point) bm.to_mesh(mesh) bm.free() Based on the frequency fluctuation data in step S2, set the parameters of the dynamic light source (such as position, intensity, color, etc.). Use dynamic light source settings (such as moving light sources or light sources with varying intensities) to simulate changes in light.

[0037] # Setting up a light source in Blender light_data = bpy.data.lights.new(name="DynamicLight", type='POINT') light_object = bpy.data.objects.new(name="DynamicLight", type='LIGHT') light_object.data = light_data bpy.context.collection.objects.link(light_object) # Dynamically update light source intensity for frame in range(start_frame, end_frame): light_object.data.energy = calculate_light_energy(frame) # Calculate light intensity based on frequency fluctuations light_object.keyframe_insert(data_path="energy", frame=frame) By combining point clouds with dynamic light sources, a dynamic power supply twin model is constructed. This ensures the model can respond to changes in light intensity in real time. A feedback mechanism is added to the model, enabling automatic adjustment of the UV power supply's operating state based on changes in scene light intensity. Dynamic adjustment is achieved using control system algorithms (such as PID control).

[0038] Step S4: Define the simulation time axis, perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis, and perform time-series radiation response change analysis to generate time-series radiation response change data of the target; In this embodiment, a simulation timeline is created to perform ordered dynamic power twin model illumination simulations. The total simulation time (e.g., seconds, minutes) and the subdivision of each time period (e.g., seconds, milliseconds) are determined. Key time points are defined on the timeline to mark the switching times of different frequency illumination conditions. For example, the frequency is set to switch at 0s, 5s, 10s, etc. The timeline data is structured, and each time point and its corresponding frequency setting are stored using a list or array for subsequent processing. Different frequency settings (e.g., 100 Hz, 200 Hz, 300 Hz) are prepared according to a preset frequency adjustment strategy. Determine the simulation software or engine to be used (such as Unity, MATLAB, Blender), and import the dynamic power twin model. Write scripts in the simulation tool or use a graphical interface to gradually adjust the frequency of the light source according to the time axis. At each key time point, update the light source frequency of the model and record the corresponding radiation output. After each frequency adjustment, record the radiation output data of the target to be processed. Through sensor simulation, data output interface or built-in functions, organize the recorded radiation response data into a time series format to ensure that the radiation value at each time point matches the corresponding frequency setting. Determine the analysis method (such as statistical analysis, chart plotting, trend analysis), select a suitable tool (such as Python's Pandas, Matplotlib or MATLAB), perform time series analysis, calculate the radiation intensity change at each time point, plot the radiation response curve, identify the impact of frequency changes on radiation output, extract key conclusions and trends from the analysis, and record the radiation response characteristics at different frequencies to support subsequent decision-making.

[0039] Step S5: Based on the preset UV radiation demand log, predict the radiation demand deviation of the target’s time-series radiation response change data, and perform radiation deviation compensation calculation to obtain the UV radiation deviation compensation value. In this embodiment, the pre-set UV radiation demand log has been compiled, containing radiation demand (such as intensity, wavelength, irradiation time, etc.) at each time point. The time-series radiation response change data of the target is compiled into a time series to ensure consistency with the timestamp of the demand log. A suitable prediction model (such as linear regression or time series analysis) is selected to predict future radiation demand deviations. The selected prediction model is trained using historical data. The demand value and actual radiation response data are input to obtain the predicted deviation value. The trained model is run to predict the radiation demand deviation at future time points, generating radiation demand deviation data. The compensation calculation method is determined, including simple additive compensation, proportional compensation, or model-based compensation methods. Based on the predicted radiation demand deviation value, the UV radiation deviation compensation value is calculated. The compensation value at each time point is recorded in the data structure to ensure the traceability of subsequent analysis and adjustment. The calculated compensation value is evaluated to determine whether it can effectively meet the UV radiation demand. If necessary, the model parameters are adjusted to optimize the compensation calculation.

[0040] Step S6: Calculate the optimal adjustment frequency for the dynamic power supply twin model based on the UV radiation deviation compensation value, and perform adaptive dynamic frequency modulation optimization to construct an adaptive dynamic frequency modulation model.

[0041] In this embodiment, UV radiation deviation compensation values, current operating frequency, and radiation demand and response data are collected, ensuring data timestamp consistency. A suitable optimization algorithm (such as genetic algorithm, particle swarm optimization, Newton's method, etc.) is selected to calculate the optimal frequency. The selected algorithm should be suitable for nonlinear and multi-peak problems. An optimization objective function is defined, typically: Objective function = UV radiation compensation value − current output. An adaptive dynamic frequency modulation model is designed, determining its inputs (such as UV radiation demand, real-time output, environmental changes, etc.) and output (adjusted frequency). A suitable control algorithm (such as PID control, fuzzy control, or adaptive control) is selected to achieve dynamic frequency adjustment. The controller should be able to adjust in real-time based on feedback. The parameters of the control algorithm (such as gain, time constant) are set according to actual conditions to ensure rapid and stable system response. Adaptive dynamic frequency modulation is implemented in the dynamic power supply twin model. Based on real-time data and feedback signals, the power supply output frequency is adjusted to meet UV radiation demand. During system operation, UV radiation output and environmental changes are continuously monitored, and the frequency is adjusted in real-time to ensure the output always meets the demand. The goal is to maximize the accuracy of radiation output and minimize deviation. The selected optimization algorithm is run, and through multiple iterations, an effective method to improve UV radiation is found. The optimal adjustment frequency for radiation output is determined, and the calculated optimal frequency is verified to effectively improve UV radiation output. The deviation between the output and the demand under the new frequency is checked. The real-time adjusted frequency and the corresponding UV radiation output are recorded in the data structure. The performance of the adaptive dynamic frequency modulation model is evaluated periodically, and the algorithm parameters are optimized.

[0042] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Acquire ambient lighting parameters from multiple directions, environmental images of the target to be processed, and preset UV radiation demand logs based on multiple sensors; Step S12: Calculate the ambient light intensity for multiple directions to obtain ambient light intensity data for multiple directions; Step S13: Perform spatial distribution analysis of ambient light intensity data from multiple directions to generate spatial distribution data of ambient light intensity; Step S14: Perform scene visual feature analysis on the environmental image of the target to be processed, and extract the current scene visual feature data; Step S15: Perform dynamic lighting rendering on the current scene visual feature data based on the ambient light intensity spatial distribution data to generate a scene light intensity distribution map.

[0043] In this embodiment, light sensors (such as photoresistors and photodiodes) and image capture devices (such as cameras) are installed at different locations to ensure that the sensors cover all directions of the area to be processed. The sensors are activated to collect ambient light parameters (such as light intensity and color temperature) and environmental images of the target in real time. A preset UV radiation demand log is recorded simultaneously, including timestamps, locations, and related parameters. The light intensity of each sensor is calculated, generating ambient light intensity data from multiple locations. This data is mapped to a three-dimensional space to generate a spatial coordinate system (X, Y, Z). Interpolation algorithms (such as Kriging interpolation and bilinear interpolation) are used to estimate the light intensity in unmeasured areas of the space, generating a complete ambient light intensity distribution map. The distribution pattern of light intensity in space is analyzed, and areas of varying light intensity are identified. Preprocessing of the environmental image, such as denoising, grayscale conversion, and edge detection, is performed to improve the accuracy of feature extraction. Computer vision techniques (such as SIFT and SURF) are then used. The system extracts visual features of the scene using either a deep learning model or a similar model, generates feature descriptors, records the position, color, shape, and other information of feature points, selects an appropriate lighting model (such as the Phong model or the Blinn-Phong model) to simulate lighting effects, combines ambient light intensity spatial distribution data with scene visual feature data, calculates the light intensity of each pixel using a rendering algorithm, and uses graphics rendering software (such as OpenGL or Unity) to perform dynamic lighting rendering and generate a scene light intensity distribution map.

[0044] In this embodiment, step S14 specifically involves the following steps: Calculate the spatial coordinates of each sensor; Based on the spatial location coordinates, multiple directional ambient light intensity data are matched to obtain ambient light intensity location matching data; Based on the location matching data of ambient light intensity, spatial distribution analysis of light intensity is performed to generate spatial distribution data of ambient light intensity.

[0045] In this embodiment, the installation positions of the sensors are determined, typically based on the geometry of the target object and the required illumination coverage. A three-dimensional coordinate system (e.g., a Cartesian coordinate system) is defined, the origin position and coordinate axis directions are determined, and the spatial position coordinates (x, y, z) of each sensor are recorded. Measurements are taken using a three-dimensional laser rangefinder or manually. The illumination intensity data of each sensor is matched with its corresponding spatial coordinates using the following method: the illumination intensity data is mapped one-to-one with the coordinates of each sensor in sequence. If there are unmeasured areas, interpolation methods (such as linear interpolation or Kriging interpolation) are used to estimate the illumination intensity. Ambient light intensity location matching data is generated, recording the coordinates of each sensor and the corresponding light intensity value. The ambient light intensity location matching data is organized into a format suitable for analysis, ensuring that each coordinate point corresponds to a light intensity value. A three-dimensional spatial model is constructed, considering the spatial location of all sensors and their corresponding light intensity data. Spatial interpolation methods (such as inverse distance weighted (IDW), Kriging interpolation, or spline interpolation) are applied to estimate the light intensity of unmeasured points, generating continuous spatial distribution data of light intensity. The generated spatial distribution data of ambient light intensity is output to form a visualized light intensity distribution map, showing the light intensity distribution in different areas.

[0046] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Perform real-time multi-point sampling of the UV inverter power supply and extract the power supply operating status parameters at multiple time points; Step S22: Perform a short-time Fourier transform on the power supply operating state parameters at multiple time points to obtain multiple time window frequency domain data; Step S23: Perform spectral distribution analysis on the frequency domain data of multiple time windows to generate spectral distribution data for each time window; Step S24: Perform dynamic time-frequency variation analysis on the spectral distribution data of each time window to generate dynamic time-frequency variation data of the power supply; Step S25: Perform real-time power frequency fluctuation fitting on the power supply dynamic time-frequency change data to construct the power supply frequency fluctuation curve.

[0047] In this embodiment, a high-precision data acquisition system (such as an oscilloscope or data acquisition card) is connected to the UV inverter power supply to ensure real-time monitoring of the power supply's operating status parameters. The sampling frequency and sampling period are configured to ensure effective acquisition of power supply operating status parameters, such as current, voltage, and power factor, at multiple time points. The data acquisition system is activated to perform real-time multi-point sampling, recording the power supply operating status parameters at multiple time points. The acquired power supply operating status parameters are stored in a data frame, ensuring that the parameters at each time point are clear and usable. An appropriate window size and overlap rate are selected, and a short-time Fourier transform analysis tool (such as the `stft` function in MATLAB) is used to perform Fourier transforms on the power supply operating status parameters at multiple time points. The signal is divided into multiple overlapping window functions, and the Fourier transform of each window function is calculated to obtain frequency domain data. Frequency domain data for multiple time windows is generated and stored in a data frame for subsequent analysis. A spectrum analysis tool (such as `Sc` in MATLAB or Python) is then used. The iPy library analyzes the frequency domain data for each time window, storing the spectral distribution data of each window in a data frame to ensure data structure for easy subsequent processing. The spectral distribution data for each time window is organized into an input format for dynamic time-frequency analysis. Time-frequency analysis techniques (such as wavelet transform or Hilbert-Huang transform) are used to dynamically analyze the spectral distribution data, calculating the change of the spectrum over time to generate dynamic time-frequency change data of the power supply, describing the change of the spectrum in the time domain. This dynamic time-frequency change data is stored in a data frame for subsequent visualization and analysis. A suitable fitting method (such as polynomial fitting, spline fitting, or least squares) is selected to fit the frequency fluctuations, performing fitting calculations on the dynamic time-frequency change data to generate a power supply frequency fluctuation curve, describing the trend of power supply frequency change. A fitting algorithm is used to perform regression analysis between the frequency data and the time data to obtain the fitted model. The constructed power supply frequency fluctuation curve is stored in a data frame and visualized for subsequent analysis and evaluation.

[0048] Example code for specific implementation: import numpy as np import matplotlib.pyplot as plt from scipy import signal from scipy.optimize import curve_fit # Simulate real-time sampling data generation function def generate_sample_data(duration, sample_rate): t = np.linspace(0, duration, int(sample_rate * duration), endpoint=False) frequency = 50 + 5 * np.sin(2 * np.pi * 1 * t) # Simulate frequency fluctuation noise = np.random.normal(0, 0.5, size=t.shape) # Add noise return t, frequency + noise # Data preprocessing functions def preprocess_data(frequency_data): # Use a low-pass filter to remove high-frequency noise b, a = signal.butter(3, 0.1) filtered_data = signal.filtfilt(b, a, frequency_data) return filtered_data # Definition of Fitting Model def model_func(t, a, b, c): return a * np.sin(b * t) + c # Implementation process if __name__ == "__main__": # 1. Obtain real-time data duration = 10 # Total time (seconds) sample_rate = 100 # Sampling rate (Hz) t, raw_frequency_data = generate_sample_data(duration, sample_rate) # 2. Data Preprocessing filtered_frequency_data = preprocess_data(raw_frequency_data) # 3. Frequency fluctuation fitting popt, pcov = curve_fit(model_func, t, filtered_frequency_data, p0=[5, 1, 50]) # 4. Results Visualization plt.figure(figsize=(12, 6)) plt.plot(t, raw_frequency_data, label='Raw Frequency Data', alpha=0.5) plt.plot(t, filtered_frequency_data, label='Filtered Frequency Data', color='orange') plt.plot(t, model_func(t, *popt), label='Fitted Curve', color='red') plt.xlabel('Time (s)') plt.ylabel('Frequency (Hz)') plt.title('UV Inverter Power Supply Frequency Fluctuation') plt.legend() plt.grid() plt.show() # 5. Print the fitted parameters print("Fitted parameters: a = {:.3f}, b = {:.3f}, c = {:.3f}".format(*popt)) In this embodiment, see Figure 4 The diagram below illustrates the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: Perform scene 3D structure analysis on the environmental image of the target to be processed, and generate scene 3D structure data of the target to be processed; Step S32: Based on the 3D structural data of the scene of the target to be processed, perform 3D point cloud modeling on the scene light intensity distribution map to construct a 3D scene model; Step S33: Perform multi-frequency power supply light propagation simulation on the UV inverter power supply based on the power supply frequency fluctuation curve to obtain light propagation simulation data for multiple frequencies; Step S34: Use light propagation simulation data of multiple frequencies to perform dynamic light simulation mapping on the 3D scene model and construct a dynamic power twin model.

[0049] In this embodiment, environmental images of the target to be processed are captured from multiple angles to ensure high image quality and sufficient scene information. A suitable 3D reconstruction algorithm is selected, such as: structured light method: reconstructing the target by projecting a known light pattern and capturing deformed images; stereo vision: calculating 3D point clouds by analyzing the parallax of images from different viewpoints. The selected algorithm is then applied to process the environmental images to generate 3D structural data of the target to be processed, and outputting 3D point cloud or mesh data. Step S31 is then completed. The generated 3D scene structure data is organized into point cloud format to ensure that the spatial coordinates of each point are clear. The scene light intensity distribution map is integrated with the point cloud data to ensure that the lighting information matches the 3D structure. 3D modeling software (such as Blender, SketchUp, or MATLAB's 3D tools) is used to perform stereo point cloud modeling. Based on the integrated point cloud data and light intensity distribution, a 3D scene model is constructed to ensure that the model can realistically reflect the geometry and lighting characteristics of the scene. The constructed 3D scene model is saved in an appropriate format (such as OBJ or STL) for subsequent analysis and visualization. A suitable light propagation simulation model is selected, such as simulating the propagation path of light in the scene and its interaction with objects. A radiative transfer model is used to consider the propagation characteristics of light at different frequencies. Based on the power frequency fluctuation curve, multi-frequency power supply light propagation simulation is performed to generate light propagation simulation data of multiple frequencies, recording the propagation path, intensity, and reflection of the light. The generated light propagation simulation data is stored in a data frame to ensure that the data structure is clear and easy to process. A suitable dynamic simulation tool or engine (such as Unity or Unreal Engine) is selected. Ray mapping is performed using Engine and Blender. Simulation data of ray propagation at multiple frequencies is used to dynamically simulate ray mapping on a 3D scene model. The frequency and intensity parameters of the light source are set to simulate the lighting effects at different frequencies. The propagation path of light in the 3D scene is calculated, and its interaction with objects (such as reflection, refraction, and absorption) is simulated. A dynamic power twin model is constructed, the results of the ray simulation mapping are recorded, and a visualization output is generated for subsequent analysis and evaluation.

[0050] In this embodiment, step S4 includes the following steps: Step S41: Define multiple time period windows and set different UV power supply frequencies for each time period; Step S42: Perform time-series window sequence fitting on multiple time-segment windows to construct a simulated time axis; Step S43: Perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis to obtain target illumination simulation response data; Step S44: Calculate the UV radiation intensity over multiple time periods from the target irradiation simulation response data to obtain multiple UV radiation intensities; Step S45: Perform target radiation response trend analysis on the target irradiation simulation response data to generate radiation response trend data for multiple time periods; Step S46: Perform time-series radiation response change analysis on radiation response trend data for multiple time periods based on multiple UV radiation intensities, thereby generating time-series radiation response change data of the target.

[0051] In this embodiment, multiple time-segment windows are defined according to experimental requirements, ensuring that the length of each window is suitable for different UV power supply frequencies. For example, short time windows are set for high-frequency power supplies, and long time windows are set for low-frequency power supplies. The start time, end time, and UV power supply operating frequency of each time-segment window are recorded to form a time-segment configuration table. The time-segment windows and their corresponding UV power supply frequencies are stored in a data frame for later use. A suitable fitting method (such as linear fitting or polynomial fitting) is selected to construct the simulation time axis. Based on the defined time-segment windows, sequential fitting of the time-series windows is performed to ensure smooth connections between time segments, forming a continuous simulation time axis. The constructed simulation time axis and its corresponding time-segment information are stored in a data frame for subsequent analysis. A dynamic power supply twin model and simulation time axis are prepared, ensuring that the model can receive irradiation at different frequencies. Light propagation simulation software (such as Blender, Unity, or a custom algorithm) is used to simulate irradiation of the target, simulating the UV irradiation effect within each time segment according to the frequency information provided by the time axis. The target's irradiation response data, including light intensity and energy distribution, are recorded within each time segment to form a target irradiation simulation response dataset. Based on the simulated irradiation response data, calculate the UV radiation intensity for each time period using physical formulas (such as the radiation intensity formula). Select appropriate trend analysis methods (such as moving average or regression analysis) to analyze the changing trend of UV radiation intensity over time. Analyze each time period based on the UV radiation intensity data to extract trend characteristics, such as maximum, minimum, and average values. Select appropriate time series analysis methods (such as difference analysis or spectral analysis) to analyze the changes in radiation response trend data. Analyze the radiation response trend data for each time period, calculating indicators such as the rate of change and the magnitude of change to assess the dynamic changes in radiation response. Compile the time series radiation response change data into reports or visualizations for further analysis and decision support.

[0052] Example code for specific implementation: Create a timeline array to store the value at each time point. import numpy as np total_time = 10 # Total simulation time (seconds) time_step = 0.1 # Time interval (seconds) time_axis = np.arange(0, total_time, time_step) Based on the frequency range defined in the dynamic power twin model, set a frequency array (such as 50Hz, 60Hz, 70Hz, etc.).

[0053] frequency_range = [50, 60, 70] # Frequency range (Hz) At each time point, an illumination simulation is run based on the selected frequency and dynamic light source parameters. The illumination changes are implemented using a physics simulation engine such as Blender or Unity.

[0054] For each time point, the irradiation effect on the target to be processed is calculated based on the current frequency and light intensity distribution map.

[0055] for frequency in frequency_range: for t in time_axis: # Adjust light source parameters adjust_light_source(frequency, t) # Custom function to adjust the light source simulate_radiation_effect(t) # Custom function to perform irradiation simulation response_data = {} for t in time_axis: response_data[t] = get_radiation_response() # Custom function to get the radiation response Choose appropriate analysis methods (such as statistical analysis and time series analysis) to assess the changing trend of radiation response.

[0056] Calculate the rate of change and magnitude of change of the radiative response at each time point.

[0057] response_changes = np.diff(list(response_data.values())) # Calculate changes Organize the recorded radiation response change data into a data frame or array, ensuring that the data format is suitable for subsequent analysis and visualization.

[0058] import pandas as pd response_df = pd.DataFrame({ 'Time': time_axis[:-1], 'Response Change': response_changes }) import matplotlib.pyplot as plt plt.plot(response_df['Time'], response_df['Response Change']) plt.xlabel('Time (s)') plt.ylabel('Radiation Response Change') plt.title('Analysis of Time-Series Radiation Response Changes') plt.grid() plt.show() Based on the time-series radiation response variation data, the impact on the target treatment effect is analyzed, and key change points and trends are identified. A detailed report is generated, including simulation parameters, response data, change analysis results, and visualization charts to facilitate subsequent decision-making and optimization.

[0059] In this embodiment, step S5 includes the following steps: Step S51: Evaluate the UV radiation effect of the target's time-series radiation response change data in multiple stages to obtain the UV radiation effect in multiple stages; Step S52: Perform multi-stage preset demand analysis on the preset UV radiation demand log and extract the radiation demand data for each stage. Step S53: Based on the radiation demand data of each stage, perform radiation demand deviation prediction on the UV radiation effect of multiple stages to obtain multi-stage UV radiation demand deviation prediction data. Step S54: Perform radiation deviation compensation calculation on the multi-stage UV radiation demand deviation prediction data to obtain the UV radiation deviation compensation value.

[0060] In this embodiment, time-series radiation response change data is collected and organized, ensuring that the data is categorized by time period. Key indicators for evaluating UV radiation effects are identified, such as radiation intensity, radiation uniformity, and irradiation time. The time-series radiation response change data is divided into multiple stages, and the UV radiation effect is evaluated stage by stage, calculating the radiation effect for each stage. Statistical analysis methods (such as mean and variance) are used to evaluate the radiation effect for each stage. Data for each stage is analyzed, the UV radiation effect for each stage is calculated, and a report is generated. A pre-set UV radiation demand log is collected, ensuring completeness and including UV radiation demand parameters for each stage. The demand log is analyzed, and radiation demand data for each stage is extracted, recording the demand value, duration, and other relevant parameters. Methods for deviation prediction (such as linear regression, time series analysis, machine learning models, etc.) are determined to facilitate demand deviation prediction. The UV radiation effect for each stage is compared with the corresponding demand data, and the deviation value (such as the difference between demand intensity and actual intensity) is calculated. A model is built using the selected prediction method, and based on existing effect and demand data, the deviation for future stages is predicted. Determine a suitable compensation calculation method, such as simple additive compensation, proportional compensation, or model-based compensation. Based on the predicted demand deviation data, calculate the UV radiation compensation value for each stage using the formula C = D − E, where C is the compensation value, D is the demand value, and E is the actual effect value. Generate a final report containing the compensation values ​​and recommendations for each stage to facilitate decision-making and implementation.

[0061] In this embodiment, the specific steps of step S54 are as follows: A stage deviation distribution analysis was performed on the multi-stage UV radiation demand deviation prediction data to generate stage deviation distribution data. The time-series distribution pattern evolution of the stage deviation distribution data is performed to obtain the radiation deviation distribution pattern data; Time-series deviation fitting is performed on the radiation deviation distribution data to construct the radiation deviation distribution curve; Deeply analyze the changes in the radiation deviation distribution curve to identify the patterns of radiation deviation. Extract the radiation demand deviation value for each stage of the multi-stage UV radiation demand deviation prediction data; The average radiation demand deviation is calculated by averaging all the radiation demand deviation values ​​to generate the average radiation demand deviation. Based on the radiation deviation law, the average value of radiation demand deviation is calculated for full-stage radiation deviation compensation, thereby obtaining the UV radiation deviation compensation value.

[0062] In this embodiment, suitable statistical analysis methods (such as histogram analysis and kernel density estimation) are selected to analyze the deviation distribution at each stage. The deviation data for each stage is analyzed to generate stage deviation distribution data. The deviation frequency and distribution characteristics of each stage are recorded. The stage deviation distribution data is organized into a time-series format to ensure that the deviation distribution information for each time period is clear. Time-series analysis tools (such as time series analysis or dynamic evolution models) are used to perform evolutionary analysis on the deviation distribution, calculate the deviation change trend between different stages, generate radiation deviation distribution trend data, and display the evolution process of deviation over time. The evolution results are recorded in a data frame and prepared for visualization to facilitate understanding of trend changes. The radiation deviation distribution trend data is organized to ensure good data structure for subsequent analysis. A suitable fitting model (such as polynomial fitting, exponential fitting, and spline fitting) is selected to construct a time-series deviation fitting model to perform time-series biasing on the radiation deviation distribution trend data. The process involves: First, a radiation deviation distribution curve is generated through poor fitting. The curve's variation is recorded, and the constructed curve is stored in a data frame for visualization. Data mining techniques (such as cluster analysis and anomaly detection) are used to analyze the curve in depth, identifying its variation patterns and characteristics, recording key change points and trends, and extracting radiation demand deviation values ​​for each stage. Data format consistency is ensured, and the extracted deviation values ​​are stored in a data frame for calculation and analysis. A suitable averaging method (such as arithmetic mean or weighted average) is selected to calculate the average value of the radiation demand deviation values. The average value of all radiation demand deviation values ​​is calculated, and a suitable compensation method (such as linear compensation, proportional compensation, or model-based compensation) is determined. Based on the radiation deviation pattern and the average demand deviation value, the UV radiation deviation compensation value is calculated using the formula: Compensation value = Average deviation × Compensation coefficient. The compensation value is recorded in a data structure, and a final report is generated for subsequent application and decision-making.

[0063] In this embodiment, step S6 is as follows: Step S61: Based on the UV radiation deviation compensation value, perform frequency iterative adjustment simulation on the dynamic power supply twin model to obtain multiple frequency adjustment strategies; Step S62: Quantize the deviation compensation for multiple frequency adjustment strategies to obtain the compensation effect of each strategy; Step S63: Calculate the optimal adjustment frequency based on the compensation effect of each strategy to obtain the optimal light source adjustment frequency; Step S64: Based on the optimal light source adjustment frequency, perform adaptive dynamic frequency adjustment optimization on the UV inverter power supply, construct an adaptive dynamic frequency adjustment model, and execute the dynamic frequency adjustment operation of the UV inverter power supply.

[0064] In this embodiment, a dynamic power twin model is established, including UV radiation deviation compensation values. Multiple initial frequency adjustment strategies are defined, such as increasing, decreasing, or maintaining the variation range of a specific frequency. Simulation software (such as MATLAB, Simulink, or other modeling tools) is used to simulate iterative frequency adjustments, recording the UV radiation output under each strategy. Each frequency adjustment strategy and its corresponding UV radiation output effect are recorded in a data structure for subsequent analysis. Evaluation criteria for the compensation effect are determined, typically including the difference between actual UV radiation and required UV radiation. The UV radiation output of each frequency adjustment strategy is compared with the preset requirement to calculate the compensation effect. Based on the compensation effect of each strategy, the optimal adjustment frequency is calculated to obtain the optimal light source adjustment frequency. A suitable optimization algorithm (such as genetic algorithm, particle swarm optimization, Newton's method, etc.) is selected to determine the optimal adjustment frequency. The compensation effect data of each strategy is used as input to evaluate the performance of each strategy. The optimization algorithm is run to calculate the optimal light source adjustment frequency that maximizes the UV radiation compensation effect. An adaptive dynamic frequency modulation model is constructed using control system design methods (such as PID control, fuzzy control, etc.). The optimal light source adjustment frequency is used as input, and the model is set to adjust the UV radiation in real time. In practical applications, the operating frequency of the variable frequency power supply is monitored by the UV radiation output and the frequency is adjusted in real time based on feedback to keep the output within the target range.

[0065] Example code for specific implementation: import numpy as np # Compensation values ​​are stored in the compensation array compensation_values ​​= np.array([...]) # Replace with actual data mean_compensation = np.mean(compensation_values) std_compensation = np.std(compensation_values) Define an objective function, typically to minimize the sum of squares or the absolute value of radiation bias.

[0066] def objective_function(frequency): # Custom function to calculate the radiation response at a given frequency response = simulate_radiation_response(frequency) return np.sum(np.square(response - target_radiation)) # Target radiation value Use optimization libraries (such as SciPy's optimize module) to find the optimal frequency.

[0067] from scipy.optimize import minimize initial_guess = mean_compensation # Initial guess result = minimize(objective_function, initial_guess, bounds=[(40, 80)]) # Set the frequency range optimal_frequency = result.x Implement a feedback mechanism in the model and use sensors to monitor UV radiation and ambient light parameters in real time.

[0068] Adjust the power supply output frequency based on feedback information, using a control algorithm (such as a PID controller) for frequency adjustment.

[0069] def adaptive_control(current_radiation, desired_radiation): error = desired_radiation - current_radiation adjustment = controller_pid(error) # PID controller output new_frequency = optimal_frequency + adjustment return new_frequency Run simulations to verify the effectiveness of the adaptive dynamic frequency modulation model. Ensure the model can respond quickly to changes in the environment and requirements.

[0070] while True: current_radiation = measure_current_radiation() # Real-time monitoring of current radiation desired_radiation = get_desired_radiation() # Get the target radiation new_frequency = adaptive_control(current_radiation, desired_radiation) set_frequency(new_frequency) # After adjusting the power supply frequency and operating for a period of time, evaluate the performance of the adaptive dynamic frequency modulation model, and record the stability and accuracy of the radiated response. Adjust model parameters, such as the gain of the PID controller, based on the evaluation results to optimize system performance. Record all adjustments and optimization processes and generate a performance report for future reference and improvement.

[0071] In this embodiment, a dynamic frequency adjustment system for an intelligent adaptive UV inverter power supply is provided, used to execute the dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply as described above, including: The dynamic lighting rendering module is used to acquire ambient lighting parameters from multiple directions, the environmental image of the target to be processed, and the preset UV radiation requirement log; based on the ambient lighting parameters from multiple directions, it performs dynamic lighting rendering on the environmental image of the target to be processed to generate a scene light intensity distribution map. The frequency fluctuation module is used to perform real-time multi-point sampling of the UV inverter power supply and fit the power supply frequency fluctuation to construct the power supply frequency fluctuation curve. The dynamic ray simulation module is used to perform 3D point cloud modeling of scene light intensity distribution map based on power frequency fluctuation curve, and to perform dynamic ray simulation mapping to build dynamic power supply twin model. The radiation response change module is used to define the simulation time axis, perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis, and perform time-series radiation response change analysis to generate time-series radiation response change data of the target. The deviation prediction module is used to predict the radiation demand deviation of the target based on the preset UV radiation demand log and to perform radiation deviation compensation calculation to obtain the UV radiation deviation compensation value. The adaptive dynamic frequency modulation module is used to calculate the optimal adjustment frequency of the dynamic power supply twin model based on the UV radiation deviation compensation value, and to perform adaptive dynamic frequency modulation optimization to build an adaptive dynamic frequency modulation model.

[0072] This invention acquires ambient lighting parameters from multiple directions, environmental images of the target object, and UV radiation demand logs to provide necessary input information for subsequent processing. Based on the ambient lighting parameters, it performs dynamic lighting rendering on the target's environmental image, generating a scene light intensity distribution map to simulate a realistic lighting environment. Real-time multi-point sampling of the UV inverter power supply is performed, and frequency fluctuation fitting is conducted to construct a power supply frequency fluctuation curve. This allows the system to more accurately understand the power supply frequency fluctuations. By constructing the power supply frequency fluctuation curve, the system can better control the stability of the power supply frequency and improve the consistency of UV radiation. Based on the power supply frequency fluctuation curve, a 3D point cloud model is created, and dynamic light simulation mapping is performed to construct a dynamic power supply twin model, improving the accuracy and realism of the lighting simulation. Dynamic light simulation mapping enables the system to better simulate light changes at different frequencies, optimizing the performance of UV radiation effects. A simulation time axis is defined, and the dynamic power supply twin model is subjected to multi-frequency... Target irradiation simulation is performed to analyze the time-series radiation response changes, generating time-series radiation response change data. This allows the system to gain a more comprehensive understanding of the changes in UV radiation effects. The analysis of time-series radiation response change data helps the system assess the impact of UV radiation on the target at different frequencies, providing a basis for subsequent frequency adjustment. Based on the UV radiation demand log, deviation prediction is performed on the time-series radiation response change data, and radiation deviation compensation calculations are performed to obtain UV radiation deviation compensation values. This allows the system to respond more accurately to changes in UV radiation demand. Radiation deviation compensation calculations optimize the UV radiation effect, enabling the system to better adapt to different demand scenarios. Based on the UV radiation deviation compensation values, the optimal adjustment frequency is calculated for the dynamic power supply twin model, performing adaptive dynamic frequency adjustment optimization and constructing an adaptive dynamic frequency adjustment model. The system intelligently adjusts the frequency to meet UV radiation demands. The adaptive dynamic frequency adjustment module optimizes the frequency adjustment of the UV inverter power supply, improving the system's energy efficiency and the stability of UV radiation effects.

[0073] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0074] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A dynamic frequency adjustment method for an intelligent adaptive UV inverter power supply, characterized in that, Includes the following steps: Step S1: Obtain ambient lighting parameters from multiple directions, environmental images of the target to be processed, and preset UV radiation requirement logs; Dynamic lighting rendering is performed on the environmental image of the target to be processed based on ambient lighting parameters from multiple directions to generate a scene light intensity distribution map. Step S2: Perform real-time multi-point sampling of the UV inverter power supply and fit the power supply frequency fluctuation to construct the power supply frequency fluctuation curve. Step S3: Based on the power frequency fluctuation curve, perform 3D point cloud modeling of the scene light intensity distribution map, and perform dynamic light simulation mapping to construct a dynamic power twin model; Step S4: Define the simulation time axis, perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis, and perform time-series radiation response change analysis to generate time-series radiation response change data of the target; Step S5: Based on the preset UV radiation demand log, predict the radiation demand deviation of the target’s time-series radiation response change data, and perform radiation deviation compensation calculation to obtain the UV radiation deviation compensation value. Step S6: Calculate the optimal adjustment frequency for the dynamic power supply twin model based on the UV radiation deviation compensation value, and perform adaptive dynamic frequency modulation optimization to construct an adaptive dynamic frequency modulation model.

2. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 1, characterized in that, The specific steps of step S1 are as follows: Step S11: Acquire ambient lighting parameters from multiple directions, environmental images of the target to be processed, and preset UV radiation demand logs based on multiple sensors; Step S12: Calculate the ambient light intensity for multiple directions to obtain ambient light intensity data for multiple directions; Step S13: Perform spatial distribution analysis of ambient light intensity data from multiple directions to generate spatial distribution data of ambient light intensity; Step S14: Perform scene visual feature analysis on the environmental image of the target to be processed, and extract the current scene visual feature data; Step S15: Perform dynamic lighting rendering on the current scene visual feature data based on the ambient light intensity spatial distribution data to generate a scene light intensity distribution map.

3. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 2, characterized in that, The specific steps of step S14 are as follows: Calculate the spatial coordinates of each sensor; Based on the spatial location coordinates, multiple directional ambient light intensity data are matched to obtain ambient light intensity location matching data; Based on the location matching data of ambient light intensity, spatial distribution analysis of light intensity is performed to generate spatial distribution data of ambient light intensity.

4. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 1, characterized in that, The specific steps of step S2 are as follows: Step S21: Perform real-time multi-point sampling of the UV inverter power supply and extract the power supply operating status parameters at multiple time points; Step S22: Perform a short-time Fourier transform on the power supply operating state parameters at multiple time points to obtain multiple time window frequency domain data; Step S23: Perform spectral distribution analysis on the frequency domain data of multiple time windows to generate spectral distribution data for each time window; Step S24: Perform dynamic time-frequency variation analysis on the spectral distribution data of each time window to generate dynamic time-frequency variation data of the power supply; Step S25: Perform real-time power frequency fluctuation fitting on the power supply dynamic time-frequency change data to construct the power supply frequency fluctuation curve.

5. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 1, characterized in that, Step S3 is as follows: Step S31: Perform scene 3D structure analysis on the environmental image of the target to be processed, and generate scene 3D structure data of the target to be processed; Step S32: Based on the 3D structural data of the scene of the target to be processed, perform 3D point cloud modeling on the scene light intensity distribution map to construct a 3D scene model; Step S33: Perform multi-frequency power supply light propagation simulation on the UV inverter power supply based on the power supply frequency fluctuation curve to obtain light propagation simulation data for multiple frequencies; Step S34: Use light propagation simulation data of multiple frequencies to perform dynamic light simulation mapping on the 3D scene model and construct a dynamic power twin model.

6. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 1, characterized in that, The specific steps of step S4 are as follows: Step S41: Define multiple time period windows and set different UV power supply frequencies for each time period; Step S42: Perform time-series window sequence fitting on multiple time-segment windows to construct a simulated time axis; Step S43: Perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis to obtain target illumination simulation response data; Step S44: Calculate the UV radiation intensity over multiple time periods from the target irradiation simulation response data to obtain multiple UV radiation intensities; Step S45: Perform target radiation response trend analysis on the target irradiation simulation response data to generate radiation response trend data for multiple time periods; Step S46: Perform time-series radiation response change analysis on radiation response trend data for multiple time periods based on multiple UV radiation intensities, thereby generating time-series radiation response change data of the target.

7. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 1, characterized in that, The specific steps of step S5 are as follows: Step S51: Evaluate the UV radiation effect of the target's time-series radiation response change data in multiple stages to obtain the UV radiation effect in multiple stages; Step S52: Perform multi-stage preset demand analysis on the preset UV radiation demand log and extract the radiation demand data for each stage. Step S53: Based on the radiation demand data of each stage, perform radiation demand deviation prediction on the UV radiation effect of multiple stages to obtain multi-stage UV radiation demand deviation prediction data. Step S54: Perform radiation deviation compensation calculation on the multi-stage UV radiation demand deviation prediction data to obtain the UV radiation deviation compensation value.

8. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 7, characterized in that, The specific steps of step S54 are as follows: A stage deviation distribution analysis was performed on the multi-stage UV radiation demand deviation prediction data to generate stage deviation distribution data. The time-series distribution pattern evolution of the stage deviation distribution data is performed to obtain the radiation deviation distribution pattern data; Time-series deviation fitting is performed on the radiation deviation distribution data to construct the radiation deviation distribution curve; Deeply analyze the changes in the radiation deviation distribution curve to identify the patterns of radiation deviation. Extract the radiation demand deviation value for each stage of the multi-stage UV radiation demand deviation prediction data; The average radiation demand deviation is calculated by averaging all the radiation demand deviation values ​​to generate the average radiation demand deviation. Based on the radiation deviation law, the average value of radiation demand deviation is calculated for full-stage radiation deviation compensation, thereby obtaining the UV radiation deviation compensation value.

9. The dynamic frequency adjustment method for the intelligent adaptive UV inverter power supply according to claim 1, characterized in that, The specific steps of step S6 are as follows: Step S61: Based on the UV radiation deviation compensation value, perform frequency iterative adjustment simulation on the dynamic power supply twin model to obtain multiple frequency adjustment strategies; Step S62: Quantize the deviation compensation for multiple frequency adjustment strategies to obtain the compensation effect of each strategy; Step S63: Calculate the optimal adjustment frequency based on the compensation effect of each strategy to obtain the optimal light source adjustment frequency; Step S64: Based on the optimal light source adjustment frequency, perform adaptive dynamic frequency adjustment optimization on the UV inverter power supply, construct an adaptive dynamic frequency adjustment model, and execute the dynamic frequency adjustment operation of the UV inverter power supply.

10. A dynamic frequency adjustment system for an intelligent adaptive UV inverter power supply, characterized in that, The method for performing dynamic frequency adjustment of the intelligent adaptive UV inverter power supply as described in claim 1 includes: The dynamic lighting rendering module is used to acquire ambient lighting parameters from multiple directions, the environmental image of the target to be processed, and the preset UV radiation requirement log; based on the ambient lighting parameters from multiple directions, it performs dynamic lighting rendering on the environmental image of the target to be processed to generate a scene light intensity distribution map. The frequency fluctuation module is used to perform real-time multi-point sampling of the UV inverter power supply and fit the power supply frequency fluctuation to construct the power supply frequency fluctuation curve. The dynamic ray simulation module is used to perform 3D point cloud modeling of scene light intensity distribution map based on power frequency fluctuation curve, and to perform dynamic ray simulation mapping to build dynamic power supply twin model. The radiation response change module is used to define the simulation time axis, perform multi-frequency target illumination simulation on the dynamic power twin model based on the simulation time axis, and perform time-series radiation response change analysis to generate time-series radiation response change data of the target. The deviation prediction module is used to predict the radiation demand deviation of the target based on the preset UV radiation demand log and to perform radiation deviation compensation calculation to obtain the UV radiation deviation compensation value. The adaptive dynamic frequency modulation module is used to calculate the optimal adjustment frequency of the dynamic power supply twin model based on the UV radiation deviation compensation value, and to perform adaptive dynamic frequency modulation optimization to build an adaptive dynamic frequency modulation model.

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