Light source intelligent annunciator control method and system

Abstract

The invention relates to the field of UV light source control, in particular to a light source intelligent annunciator control method and system. The method comprises the following steps: acquiring environment illumination sensing parameters of a to-be-cured material, performing light intensity spatial and temporal distribution analysis and dynamic light intensity distribution change fitting, and constructing a curing environment light intensity spatial and temporal distribution change diagram; collecting light source parameters of the UV lamp bead array; performing corresponding lamp bead light source parameter adjustment on the light source parameters according to a curing environment light intensity space-time distribution change diagram to obtain a differential UV light source parameter adjustment strategy; and performing real-time curing irradiation on the to-be-cured material according to the differential UV light source parameter adjustment strategy, adjusting the curing light source intensity and the curing time, and constructing an intelligent curing control strategy. Through real-time and dynamic UV light source control of the annunciator, the UV curing efficiency and quality are improved.

Description

Technical Field

[0001] The present invention relates to the field of UV light source control, and particularly to a control method and system for an intelligent signal machine of a light source. Background Art

[0002] With the continuous development of industrial production technology, due to its characteristics of high efficiency, environmental protection and energy saving, UV curing technology has been widely used in many fields such as coatings, printing, and electronic manufacturing. Especially in the context of the increasing demand for precision machining, rapid production and high-quality control, the importance of UV curing technology has become even more prominent. Compared with traditional thermal curing or other curing methods, UV curing can cure materials through an ultraviolet light source in a short time, thus greatly improving production efficiency and reducing energy consumption.

[0003] However, although UV curing technology has significant advantages in improving production efficiency and environmental friendliness, in practical applications, the stability and reliability of its curing effect still face many challenges. The intensity, wavelength, uniformity of the UV light source and the timeliness during the curing process directly affect the quality of the final curing effect. With the diversification of product types and curing requirements, traditional UV curing light source control methods can no longer meet the precise and intelligent curing requirements. Especially affected by factors such as environmental light conditions, material characteristics and production line changes, it is difficult to ensure that the curing effect of each batch can meet high-standard requirements by the traditional setting method of fixed light source intensity and exposure time.

[0004] Currently, existing UV curing light source control methods usually rely on a simple timing and quantitative control mode, and execute the curing process by setting fixed light source intensity and curing time. Although these methods are effective in some basic application scenarios, due to the lack of real-time perception and intelligent adjustment of environmental changes and curing material differences, they often lead to uneven curing, low efficiency, and even curing defects. This not only reduces production efficiency, but also causes waste of resources and instability of product quality. In order to meet the high requirements of modern manufacturing for curing quality and efficiency, an intelligent light source control method is urgently needed. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes a control method and system for an intelligent signal machine of a light source to solve at least one of the above technical problems.

[0006] To achieve the above object, the present invention provides a control method for an intelligent signal machine of a light source, including the following steps: Step S1: Obtain the environmental light sensing parameters of the material to be cured, perform analysis on the spatio-temporal distribution of light intensity and fitting of the dynamic light intensity distribution change, and construct a spatio-temporal distribution change diagram of the environmental light intensity for curing; Step S2: Collect the light source parameters of the UV lamp bead array; adjust the light source parameters of the corresponding lamp beads according to the spatio-temporal distribution change diagram of the curing ambient light intensity to obtain a differential UV light source parameter adjustment strategy; Step S3: Perform real-time curing irradiation on the material to be cured according to the differential UV light source parameter adjustment strategy, and adjust the curing light source intensity and curing time to construct an intelligent curing control strategy; Step S4: Based on the intelligent curing control strategy, conduct real-time curing state mining and rolling prediction of the curing situation in the future period to generate the curing situation prediction diagrams of multiple time windows; Step S5: Obtain the preset curing log, detect the curing deviation area of the material curing situation prediction diagram, and perform deviation compensation calculation to obtain the adaptive curing compensation parameter; Step S6: Perform iterative collaborative control optimization according to the adaptive curing compensation parameter and the intelligent curing control strategy to construct an intelligent collaborative curing light source control model.

[0007] By obtaining the sensing data of the ambient light, including light intensity, light source angle, wavelength, etc., the present invention can perceive the changes in the curing environment in real time. Especially when there are changes in the production environment (such as changes in ambient light, temperature and humidity, etc.), the real-time monitoring of the ambient light provides data support for subsequent optimization. The analysis of the spatio-temporal distribution of light intensity can help identify the periodicity and randomness of the changes in ambient light, and provide the key light intensity distribution patterns. This can discover potential light unevenness problems in advance and ensure more accurate adjustment of the light source during the curing process. Through the dynamic light intensity distribution change fitting model, it can adapt to different curing environments according to the real-time collected data, and realize intelligent response to environmental changes. It can ensure the stability of the curing process under different production conditions. By collecting the specific light source parameters of the UV lamp bead array (such as power, wavelength, irradiation angle, etc.), it can ensure the accuracy and stability of the output of each lamp bead. Based on the ambient light data, the system can make targeted adjustments to avoid the interference of ambient light changes on the curing effect. The spatio-temporal change map of the ambient light enables the light source parameters of each curing area to be differentially adjusted according to specific needs, ensuring the optimization of the curing effect in each area. For example, in some areas where the ambient light is strong, the power of the UV lamp beads needs to be reduced; while in areas with weak light, the power needs to be increased or the irradiation time needs to be adjusted. This differential UV light source parameter adjustment strategy can respond to environmental changes in real time, ensure that the light source output during the curing process always remains within an ideal range, and ensure the stability of the curing quality. By adjusting the intensity and exposure time of the UV light source according to the real-time feedback, it can achieve precise control of the curing process. At different curing stages, the reasonable adjustment of the light source intensity and curing time can avoid over-curing or under-curing, and improve the curing quality and production efficiency. According to the differential adjustment strategy, the system can make intelligent adjustments according to the reaction of the material, making the entire curing process more automated and intelligent. Through deep learning and control strategies, the system can automatically identify the curing requirements of the material and optimize the parameters. Since the system realizes automatic adjustment and does not require frequent manual intervention, the risk of human errors and operation mistakes is reduced. The precise control of the curing effect further improves the stability and consistency of the curing quality. Through the real-time mining of the curing state, the system can continuously track the key indicators during the curing process (such as temperature, light intensity, color change, etc.), and monitor the progress and quality of the curing in real time. It can discover potential problems in time during the curing process and make adjustments. Based on the historical data and the current curing state, the system can conduct a rolling prediction of the curing situation in the future time period. Such a prediction can help discover curing problems in advance and take measures for prevention. The prediction maps of multiple time windows can show the curing state trends in different time periods, so as to further optimize the entire production process. By accurately predicting the curing situation, the production scheduling can be more flexible and efficient. For example, predicting the occurrence of curing abnormalities or deviations, other links in the production process can also be adjusted according to the curing progress to ensure the overall smoothness of the production line.By analyzing the curing logs and the curing trend prediction diagrams, the system can promptly identify the deviation areas during the curing process. This deviation detection can avoid the defective areas that occur during the curing process, ensuring the consistency and high quality of the products. Based on the deviation compensation calculation, the system can automatically adjust the light source parameters according to the detected curing deviations, ensuring that every link in the curing process can be corrected in a timely manner. This adaptive compensation mechanism can significantly improve the accuracy of curing, especially in complex or changing production environments. Deviation compensation can effectively ensure the precise control of the curing process, making quality adjustments in a timely manner during the production process and reducing material waste and rework costs. The combination of intelligent curing control strategies and adaptive curing compensation parameters forms an iterative optimization mechanism. Through continuous learning and optimization, the system makes the light source adjustment during the curing process more precise and intelligent. Over time, the system can optimize the control strategy under different production environments and material characteristics, improving the overall curing effect. The collaborative work between different UV light sources and signal machines will be further optimized. Through the collaborative control of multiple light sources, it can ensure that the light source output in each area during the curing process is uniform, avoiding over-curing or under-curing. This collaborative optimization helps to improve production efficiency and quality consistency. The system can flexibly adjust the control strategy according to changes in the curing tasks, production environments, and equipment states. It greatly improves the automation level during the production process, reduces the need for manual intervention, and at the same time ensures the efficiency and consistency of the production process.

[0008] In this specification, a light source intelligent signal machine control system is provided for implementing the light source intelligent signal machine control method described above, including: The light intensity spatio-temporal distribution module is used to obtain the ambient light sensing parameters of the material to be cured, perform light intensity spatio-temporal distribution analysis and fitting of dynamic light intensity distribution changes, and construct a spatio-temporal distribution change diagram of the curing ambient light intensity; The differential adjustment module is used to collect the light source parameters of the UV lamp bead array; adjust the light source parameters of the corresponding lamp beads according to the spatio-temporal distribution change diagram of the curing ambient light intensity to obtain a differential UV light source parameter adjustment strategy; The curing parameter adjustment module is used to perform real-time curing irradiation on the material to be cured according to the differential UV light source parameter adjustment strategy, and adjust the curing light source intensity and curing time to construct an intelligent curing control strategy; The curing trend prediction module is used to perform real-time curing state mining and rolling prediction of the curing trend in the future period based on the intelligent curing control strategy, and generate a curing trend prediction diagram of the material for multiple time windows; The adaptive compensation module is used to obtain the preset curing logs, detect the curing deviation areas in the curing trend prediction diagram of the material, and perform deviation compensation calculation to obtain adaptive curing compensation parameters; The collaborative control optimization module is used to perform iterative collaborative control optimization according to the adaptive curing compensation parameters and the intelligent curing control strategy, and construct an intelligent collaborative curing light source control model.

[0009] By collecting environmental light data (such as light intensity, light source angle, wavelength, etc.), the present invention can comprehensively understand the light conditions of the environment where the material to be cured is located. Since the light intensity and distribution mode are different in different production environments, this module provides an accurate modeling of the spatio-temporal changes in environmental light intensity, which helps the subsequent adjustment of the light source. Through the analysis of the spatio-temporal distribution of light intensity, anomalies in the light environment can be found (for example, the light intensity in some areas is too low or too high), providing data support for the subsequent adjustment. The fitting of dynamic light intensity changes can monitor environmental changes in real time, give early warnings and provide a basis for system adjustment. The spatio-temporal distribution change map of light intensity provides dynamic environmental adaptation data, enabling the system to automatically adjust the control strategy according to environmental changes, so as to ensure that the UV light source can work continuously and stably under variable environmental conditions and ensure the consistency of curing quality. By collecting and analyzing the light source parameters (such as power, radiation angle, wavelength, etc.) of each UV lamp bead array in real time, the output of each lamp bead can be adjusted individually according to the environmental light data. The curing requirements of each area are different, and the differential light source parameter adjustment strategy can avoid unnecessary energy waste and ensure uniform light intensity distribution during the curing process. The environmental light intensity change map provides real-time light intensity and distribution information of different areas, and the differential adjustment module can adjust the light source according to this information, ensuring that the light source power is reduced in high-light areas and increased in low-light areas, thus avoiding problems of over-curing or insufficient curing. According to the light change map, the system can intelligently respond to the needs of different curing areas and perform dynamic adjustment, so as to ensure the stability and efficiency of the curing process and improve the production quality. It can monitor the light source intensity and curing time during the curing process in real time and make flexible adjustments according to environmental light changes and the curing requirements of the material. Real-time adjustment can ensure the optimization of the curing process and improve the curing quality of the material. By establishing an intelligent curing control strategy, the system can automatically adjust the curing parameters (such as light source intensity, exposure time, etc.) at different time periods to cope with the changes in environmental light intensity and the different reactions of the curing material, realizing the intelligent coordination of the light source and the curing process. This module can adjust the exposure time and light source intensity according to the real-time reaction of the material, ensuring that the curing process is neither excessive nor insufficient, thus avoiding production defects caused by insufficient curing or over-curing. By mining the curing state in real time, anomalies or deviations in the curing process can be identified in time and adjustments can be made quickly. In addition, the real-time feedback of the curing state provides data support for subsequent optimization. By rolling and predicting the curing trend within multiple time windows, the system can discover potential problems in advance and make adjustments. For example, predicting over-cured areas or under-cured areas that appear, so as to adjust the curing strategy in time. Through the future prediction of the curing trend, the scheduling and arrangement in the production process can be optimized in advance, avoiding unnecessary downtime or adjustment, improving production efficiency and reducing the stagnation in the production process.By analyzing the curing logs and comparing them with the curing trend prediction chart, the system can detect deviation areas during the curing process in a timely manner, such as over-curing or under-curing in certain areas, ensuring precise control of the entire curing process. Based on the deviation areas, the system can dynamically calculate compensation parameters and automatically adjust the light source intensity or exposure time. Through real-time feedback and compensation, overall quality problems caused by local deviations are avoided. Adaptive compensation can significantly improve the consistency of curing quality, ensuring that the same curing effect can be obtained in different areas of the product. The compensation strategy can optimize the curing process and improve the level of product quality control. By continuously iteratively optimizing the light source control strategy, parameters such as light source power and exposure time are gradually improved to better adapt to changes in the production environment and materials. This optimization process can improve the curing effect based on historical data and real-time feedback. By coordinately controlling different light sources, the module can ensure that the curing effects in different areas are consistent, avoiding quality differences caused by uneven light sources or insufficient power. Coordinated optimization also helps to improve the energy efficiency and stability of the entire production process. The module combines adaptive compensation and intelligent control strategies to ensure the coordinated operation of the light source and the curing process, thus guaranteeing the efficiency and stability of the curing process. The optimized control strategy reduces the need for manual intervention and further improves the automation level. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 It is a schematic flowchart of the steps of a method for controlling an intelligent signal machine of a light source according to the present invention; Figure 2 It is a schematic flowchart of the detailed implementation steps of step S1; Figure 3 It is a schematic flowchart of the detailed implementation steps of step S2; Figure 4 It is a schematic flowchart of the detailed implementation steps of step S3. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0011] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0012] The embodiments of the present application provide a method and system for controlling an intelligent signal machine of a light source. The execution subjects of the method and system for controlling an intelligent signal machine of a light source include but are not limited to: mechanical equipment, data processing platforms, cloud server nodes, network uploading devices, etc. that carry this system, which can be regarded as general computing nodes of the present application. The data processing platform includes but is not limited to: at least one of an audio and image management system, an information management system, and a cloud data management system.

[0013] Please refer to Figures 1 to 4 , the present invention provides a method for controlling an intelligent signal machine of a light source, and the method for controlling an intelligent signal machine of a light source includes the following steps: Step S1: Obtain the ambient light sensing parameters of the material to be cured, conduct spatio-temporal distribution analysis of light intensity and fitting of dynamic light intensity distribution changes, and construct a spatio-temporal distribution change diagram of the curing environment light intensity; Step S2: Collect the light source parameters of the UV lamp bead array; adjust the corresponding lamp bead light source parameters according to the spatio-temporal distribution change diagram of the curing environment light intensity to obtain a differential UV light source parameter adjustment strategy; Step S3: Perform real-time curing irradiation on the material to be cured according to the differential UV light source parameter adjustment strategy, and adjust the curing light source intensity and curing time to construct an intelligent curing control strategy; Step S4: Based on the intelligent curing control strategy, conduct real-time curing state mining and rolling prediction of the curing trend in the future period, and generate curing trend prediction diagrams of the material for multiple time windows; Step S5: Obtain the preset curing log, detect the curing deviation area of the material curing trend prediction diagram, and perform deviation compensation calculation to obtain an adaptive curing compensation parameter; Step S6: Perform iterative collaborative control optimization according to the adaptive curing compensation parameter and the intelligent curing control strategy to construct an intelligent collaborative curing light source control model.

[0014] The present invention can sense the changes in the curing environment in real time by obtaining the sensing data of the ambient light, including light intensity, light source angle, wavelength, etc. Especially when there are changes in the production environment (such as changes in ambient light, temperature and humidity, etc.), the real-time monitoring of the ambient light provides data support for subsequent optimization. The analysis of the spatio-temporal distribution of light intensity can help identify the periodicity and randomness of the changes in ambient light and provide key light intensity distribution patterns. This can detect potential light unevenness problems in advance and ensure more precise adjustment of the light source during the curing process. Through the dynamic light intensity distribution change fitting model, it can adapt to different curing environments according to the real-time collected data and achieve intelligent response to environmental changes. It can ensure the stability of the curing process under different production conditions. By collecting the specific light source parameters of the UV lamp bead array (such as power, wavelength, irradiation angle, etc.), it can ensure the accuracy and stability of the output of each lamp bead. Based on the ambient light data, the system can make targeted adjustments to avoid the interference of ambient light changes on the curing effect. The spatio-temporal change diagram of the ambient light enables the light source parameters of each curing area to be differentially adjusted according to specific needs to ensure the optimization of the curing effect in each area. For example, in some areas where the ambient light is strong, the power of the UV lamp beads needs to be reduced; while in areas with weak light, the power needs to be increased or the irradiation time needs to be adjusted. This differential UV light source parameter adjustment strategy can respond to environmental changes in real time and ensure that the light source output during the curing process always remains within an ideal range to ensure the stability of the curing quality. Adjusting the intensity and exposure time of the UV light source according to real-time feedback can achieve precise control of the curing process. At different curing stages, the reasonable adjustment of the light source intensity and curing time can avoid over-curing or under-curing, improve the curing quality and production efficiency. According to the differential adjustment strategy, the system can make intelligent adjustments according to the reaction of the material, making the entire curing process more automated and intelligent. Through deep learning and control strategies, the system can automatically identify the curing requirements of the material and optimize the parameters. Since the system realizes automatic adjustment and does not require frequent manual intervention, the risk of human errors and operation mistakes is reduced. The precise control of the curing effect further improves the stability and consistency of the curing quality. Through the real-time mining of the curing state, the system can continuously track the key indicators during the curing process (such as temperature, light intensity, color change, etc.) and monitor the progress and quality of the curing in real time. It can detect potential problems in time during the curing process and make adjustments. Based on historical data and the current curing state, the system can conduct rolling prediction of the curing trend in the future period. Such prediction can help detect curing problems in advance and take measures for prevention. The prediction diagrams of multiple time windows can show the curing state trends in different time periods, thereby further optimizing the entire production process. Through precise prediction of the curing trend, production scheduling can be more flexible and efficient. For example, predicting curing anomalies or deviations, other links in the production process can also be adjusted according to the curing progress to ensure the overall smoothness of the production line.By analyzing the curing logs and curing trend prediction diagrams, the system can promptly identify the deviation areas during the curing process. This deviation detection can avoid defective areas that may occur during the curing process, ensuring product consistency and high quality. Based on the deviation compensation calculation, the system can automatically adjust the light source parameters according to the detected curing deviations, ensuring that every link in the curing process can be corrected in a timely manner. This adaptive compensation mechanism can significantly improve the curing accuracy, especially in complex or changing production environments. Deviation compensation can effectively ensure the precise control of the curing process, make quality adjustments in a timely manner during the production process, and reduce material waste and rework costs. The combination of intelligent curing control strategies and adaptive curing compensation parameters forms an iterative optimization mechanism. Through continuous learning and optimization, the system makes the light source adjustment during the curing process more precise and intelligent. Over time, the system can optimize the control strategy under different production environments and material characteristics, improving the overall curing effect. The coordinated operation between different UV light sources and signal machines will be further optimized. Through the coordinated control of multiple light sources, it can ensure uniform light source output in each area during the curing process, avoiding over-curing or under-curing. This coordinated optimization helps improve production efficiency and quality consistency. The system can flexibly adjust the control strategy according to changes in curing tasks, production environments, and equipment status. This greatly improves the automation level during the production process, reduces the need for manual intervention, and at the same time ensures the efficiency and consistency of the production process.

[0015] In the embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of a method for controlling an intelligent signal machine of a light source according to the present invention. In this example, the steps of the method for controlling an intelligent signal machine of a light source include: Step S1: Obtain the ambient light sensing parameters of the material to be cured, perform an analysis of the spatio-temporal distribution of light intensity and fit the dynamic change of light intensity distribution, and construct a spatio-temporal distribution change diagram of the ambient light intensity during curing; In this embodiment, a suitable light sensor is selected. Usually, a photodiode, a photoresistor, or a spectral sensor is used, which can effectively measure the light intensity in the environment. Ensure that the sensor has sufficient sensitivity and dynamic range to adapt to the working conditions of the UV light source. Configure the sampling frequency and measurement range of the sensor. Set to collect light intensity data once per second, and ensure that the sensor can cover the required wavelength range (such as 200nm to 400nm) to capture the intensity of UV light. In the curing environment of the material to be cured, start the light sensor and record the ambient light intensity data in real time. Ensure that the time period of data collection can cover all stages of the curing process. Usually, it is recommended to collect data for at least 10 minutes. Record the format of the data, including the timestamp, the ambient light intensity (such as mW / cm²), and the position of the sensor. Data examples are as follows: the light intensity is 20mW / cm² at t = 0 seconds, and the light intensity is 35mW / cm² at t = 60 seconds. After the data collection is completed, check the integrity and accuracy of the data to ensure that there are no missing sampling points or outliers. Statistical methods (such as mean and standard deviation) can be used to identify abnormal data. Record the correction process. For example, if the light intensity value at a certain time point is abnormal, denoising processing is required to ensure the reliability of the final data set. Organize the collected ambient light data into a structured format for subsequent analysis. You can choose to use an Excel table or a database system to correspond the timestamp with the corresponding light intensity value. Perform preprocessing of the data, including removing noise and filling in missing values. Use moving average or linear interpolation methods to smooth the data so that the trend of light intensity change is more obvious. Select a suitable spatio-temporal distribution analysis method. For example, spatial interpolation methods (such as Kriging interpolation) can be used to estimate the light intensity distribution at different positions and conduct dynamic analysis in combination with time factors. Determine the analysis time window. For example, every 5 seconds is a time window, and analyze the change in light intensity within this time window. Use statistical software (such as MATLAB, Python, etc.) to conduct spatio-temporal distribution analysis on the organized data and generate a heat map or 3D chart of the light intensity distribution. Ensure that the chart can intuitively display the change of light intensity over time and space. Record the analysis results. For example, within a certain period, the highest light intensity distribution at a certain position can reach 50mW / cm², indicating that this area is the light intensity concentration area. According to the characteristics of the spatio-temporal distribution change of light intensity, select a suitable fitting model. Commonly used models include polynomial regression models, exponential decay models, or sine wave models, which can effectively describe the dynamic change of light intensity. Determine the initial parameters of the model. For example, select the order of the polynomial or the time constant of exponential decay for fitting. Use a non-linear regression analysis tool to perform dynamic fitting on the light intensity data to obtain a mathematical expression of the light intensity change. Record the goodness-of-fit index (such as R² value) to evaluate the accuracy of the fitting. For example, if the fitting result shows that the relationship between light intensity and time is I(t)=20 + 15e^(-0.1t) and the R² value is 0.95, it indicates that the fitting effect is good.Compare the fitting results with the original data to generate a visualization chart of the light intensity change. The chart should include the original data points and the fitting curve to facilitate an intuitive understanding of the light intensity change trend. Record the analysis results, such as the light intensity value after fitting is 32 mW / cm² at t = 30 seconds, indicating that the ambient light intensity changes dynamically over time. Based on the analysis of the spatio-temporal distribution of light intensity and the fitting results of the dynamic light intensity distribution change, design and construct a spatio-temporal distribution change map of light intensity. The chart should clearly show the changes in light intensity at different times and spatial positions. Select a suitable chart type, such as a heat map, a three-dimensional surface chart, or an animation chart, to facilitate the display of the dynamic change of light intensity. Use data visualization tools (such as Tableau, Matplotlib, etc.) to generate the spatio-temporal distribution change map of light intensity. Ensure that the colors and markings of the chart can clearly distinguish different light intensity levels. Optimize the format of the chart, such as adjusting the axis labels, legends, and titles, to ensure that the chart information is accurate and easy to read. Save and record the generated spatio-temporal distribution change map of light intensity to form a reference for the solidified ambient light intensity distribution. Analyze the key features in the chart, such as the light intensity concentration trend and change rules in specific regions.

[0016] Step S2: Collect the light source parameters of the UV lamp bead array; adjust the corresponding lamp bead light source parameters according to the spatio-temporal distribution change map of the solidified ambient light intensity to obtain a differential UV light source parameter adjustment strategy; In this embodiment, a system is configured to collect the light source parameters of the UV lamp bead array. The system should include multiple high-sensitivity light intensity sensors that can measure the light intensity output (such as mW / cm²) of each lamp bead respectively and have an automatic recording function. Determine the sampling frequency, which is recommended to be set to collect once per second to capture the light intensity changes of the lamp beads under different working conditions. Ensure that the installation positions of the sensors can effectively cover all lamp beads to avoid occlusion. Before collection, set the state of the lamp bead array to ensure that each lamp bead can be controlled individually. Turn on the lamp beads one by one through the control system and record the light source parameters of each lamp bead, including power output, wavelength, irradiation time, etc. For example, if the light intensity of lamp bead A is 400 mW / cm² and the light intensity of lamp bead B is 350 mW / cm², ensure that the corresponding timestamps and lamp bead numbers are recorded for subsequent analysis. View the spatio-temporal distribution change diagram of the cured ambient light intensity generated from step S1 to identify the distribution characteristics of the light intensity in different regions and at different times. Focus on the regions with concentrated light intensity and weak regions for targeted adjustment of lamp bead parameters. Record the key data of the light intensity distribution. For example, during the curing process, the light intensity value in a certain region reaches 50 mW / cm², while in another region it is only 20 mW / cm², which will provide a basis for subsequent adjustment strategies. According to the analysis results of the light intensity distribution map, formulate a differentiated UV light source parameter adjustment strategy. This strategy should consider the specific position of each lamp bead and its corresponding light intensity requirements to optimize the curing effect. For example, for the region with weak light intensity, it is necessary to increase the power output of the lamp beads in this region (such as increasing from 350 mW / cm² to 450 mW / cm²), while for the region with too high light intensity, the power output should be considered to be reduced to avoid over-curing. Through the light source control system, adjust the light source parameters of the lamp beads one by one. According to the formulated strategy, precisely adjust the power output of each lamp bead and record the parameters of each adjustment. For example, adjust the light intensity of lamp bead C from 400 mW / cm² to 450 mW / cm² while keeping the light intensity of lamp bead D at 350 mW / cm² to ensure uniform light intensity distribution in the entire curing area. After completing the adjustment of the lamp bead parameters, conduct real-time monitoring to verify the adjusted light source output. Use the light intensity sensors to re-measure the light intensity output of each lamp bead to ensure that it meets the expected adjustment requirements. Record the adjusted light intensity values and effect evaluations. For example, after adjustment, the light intensity of lamp bead C is 450 mW / cm² and the curing effect is significantly improved, forming a final adjustment report.

[0017] Step S3: According to the differentiated UV light source parameter adjustment strategy, perform real-time curing irradiation on the material to be cured, and adjust the curing light source intensity and curing time to construct an intelligent curing control strategy; In this embodiment, before performing real-time curing irradiation, ensure the stability of the curing environment. Check the status of the UV curing equipment, including the working conditions of the lamp beads, the cooling system of the light source, and the functions of the sensors, to ensure that all equipment is operating normally and avoid unexpected situations during the curing process. Set up the curing platform to ensure that the material to be cured is placed stably, to avoid uneven illumination caused by movement during the curing process. Record the environmental parameters, such as temperature and humidity, for subsequent analysis. According to the differential UV light source parameter adjustment strategy, set the real-time light source intensity of each lamp bead. Ensure that the light source intensity conforms to the previously established parameters. For example, the lamp beads in the area with weak light intensity output 450 mW / cm², while those in the area with concentrated light intensity remain at 350 mW / cm². Through the intelligent control system, adjust the light source output of each lamp bead one by one, and monitor its light intensity value in real time to ensure that the output of each lamp bead meets the expectations. Record the initially set light source intensity and time. According to the characteristics of the material to be cured and the required curing effect, set an appropriate curing time. For example, for materials with high viscosity, a longer irradiation time (such as 30 seconds) is required, while for materials with low viscosity, the irradiation time can be appropriately shortened (such as 15 seconds). During the curing process, monitor the curing status of the material in real time. If uneven curing or a curing progress that does not meet expectations is found, the curing time can be dynamically adjusted according to the actual situation. Design a dynamic monitoring and feedback mechanism to collect relevant data such as light source intensity, curing time, and curing effect during the curing process in real time. Use light intensity sensors and temperature sensors to monitor the status of the curing area to ensure accurate real-time data is obtained. Determine the feedback frequency, such as collecting data once per second, to promptly adjust the curing parameters. Record the data of each feedback, including the timestamp, light intensity value, and temperature. According to the real-time monitored data, analyze whether the curing status reaches the expected effect. If the monitored curing effect is not ideal (for example, the curing thickness is insufficient or the surface is not fully cured), the light source intensity or curing time needs to be quickly adjusted. For example, if the curing effect in a certain observed area is not ideal, the system can automatically increase the light source intensity of the lamp beads in that area or extend the irradiation time to ensure curing uniformity. Implement intelligent control algorithms, such as fuzzy control or PID control algorithms, to ensure the stability and uniformity of the curing process. The control algorithm should be able to automatically adjust the light source intensity and curing time according to the real-time monitored data. For example, if the light intensity sensor feedbacks that the light intensity in a certain area is lower than the set value, the control algorithm will automatically increase the light source output of the lamp beads in that area to ensure the adaptive adjustment of the curing process. During the curing process, regularly monitor the curing effect and evaluate the curing uniformity and strength. The curing effect can be evaluated through physical tests (such as hardness tests, adhesion tests) or spectral analysis. Record the results of each test, including the hardness value, adhesion value after curing, and relevant experimental conditions, for subsequent analysis.

[0018] Step S4: Based on the intelligent curing control strategy, conduct real-time curing state mining and rolling prediction of the curing trend in the future period, and generate curing trend prediction diagrams for multiple time windows; In this embodiment, a real-time data acquisition system is configured to ensure continuous monitoring of various parameters during the curing process, including light source intensity, curing time, temperature, humidity, and the hardness of the curing material, etc. High-precision sensors are used to ensure the accuracy and reliability of the data. Determine the sampling frequency, which is recommended to be set to once per second to facilitate capturing the dynamic changes during the curing process. Record the data format, including timestamps and the values of each monitored parameter. Select a suitable curing state mining method, usually using machine learning algorithms (such as support vector machines, random forests) or statistical analysis methods to analyze the relationship between the real-time monitoring data and the curing effect. Determine the input features of the model, such as light source intensity, curing time, and temperature, etc., and set the corresponding output targets (such as curing effect score or curing uniformity) for subsequent modeling and prediction. Use the selected algorithm to analyze the real-time monitoring data to mine the characteristics and laws of the curing state. By training the model, identify the key factors affecting the curing effect and evaluate the current curing state. For example, if the analysis results show a significant positive correlation between the light source intensity and the curing uniformity, the light source output strategy can be optimized accordingly to ensure the improvement of the curing effect. Based on the real-time monitoring data and the mined curing state characteristics, construct a curing trend prediction model for the future period. Time series analysis methods (such as ARIMA models) or regression analysis methods can be selected to predict the future curing state. Determine the parameter settings of the model, such as the selected time window length (such as 5 seconds or 10 seconds) for effective rolling prediction. Record the predicted target variables, such as curing uniformity or curing effect score. During the curing process, input the current monitoring data into the prediction model in real time for rolling prediction. According to the model output, predict the curing state changes in the next few periods. For example, if the current curing state is good, predict that the curing uniformity will remain at 0.02 within the next 5 seconds and rise to 0.03 within 10 seconds, and record the prediction results and confidence intervals for each period. According to the rolling prediction results, design and construct a curing trend prediction diagram. Ensure that the chart can clearly display the curing state changes within different time windows, including light source intensity, curing uniformity, and other key parameters.

[0019] Step S5: Obtain the preset curing log, detect the curing deviation area of the curing trend prediction diagram of the material, and perform deviation compensation calculation to obtain the adaptive curing compensation parameter; In this embodiment, the preset curing log is extracted from the UV curing control system. The log should contain key parameters during the curing process, such as data on light source intensity, irradiation time, ambient temperature, humidity, and curing effect evaluation. The extracted log is sorted to ensure consistent data formats, including timestamps, numerical values and statuses of various parameters (such as whether the curing is successful). For example, record that the light source intensity is 400 mW / cm², the irradiation time is 30 seconds, and the curing effect evaluation is "good". After sorting, check the integrity of the curing log to ensure there are no missing data points or outliers. Statistical analysis can be used to identify abnormal situations. For example, calculate the mean and standard deviation of the light source intensity. If a value exceeds the set range, it is marked as abnormal. If data anomalies are found, such as the light source intensity being zero at a certain time point, a review is required to ensure the accuracy and usability of the data for subsequent analysis. Obtain the previously generated material curing trend prediction diagram, which shows the curing uniformity and light intensity distribution within different time windows. Pay close attention to the light intensity concentration areas and deviation areas marked in the diagram for subsequent detection. Record the key information in the prediction diagram. For example, the predicted curing uniformity value in a certain area is 0.05, indicating a curing deviation in that area. Select a suitable deviation area detection method, usually the threshold method, to identify the curing deviation area according to the set uniformity standard (such as 0.05). If the curing uniformity in a certain area exceeds the set range, it is marked as a deviation area. Determine the specific detection criteria. For example, set the uniformity less than 0.05 as normal and greater than 0.05 as deviation for subsequent analysis and compensation. Analyze the curing trend prediction diagram and mark all deviation areas. For example, if the uniformity in a certain area is 0.06, then this area is marked as a "curing deviation area". Record the specific location of the deviation area and its uniformity value for subsequent compensation calculations. Form a report detailing the characteristics of each deviation area. According to the detected deviation areas, select a suitable compensation calculation method. An algorithm based on feedback control, such as PID control, can be used to dynamically adjust the light source output power to compensate for the deviation. Determine the compensation strategy. For example, increase or decrease the light source intensity in the deviation area, and record the adjustment amplitude and target value for each area. For each deviation area, calculate the required compensation value. For example, if the light intensity value in the deviation area is 300 mW / cm² and the target light intensity value is 400 mW / cm², then the light source output needs to be increased by 100 mW / cm². Record the calculation process of the compensation parameters, including the light intensity values before and after compensation, the compensation amount, and the relevant time points for subsequent verification. Record the calculated compensation parameters as adaptive curing compensation parameters. These parameters should be able to be dynamically adjusted according to the real-time state during the curing process to ensure the uniformity and stability of the curing effect.For example, the formed compensation parameters are as follows: the light source output in the deviation area A needs to be increased to 400 mW / cm², and the light source output in the deviation area B needs to be decreased to 350 mW / cm² to ensure uniform curing in each area.

[0020] Step S6: Perform iterative collaborative control optimization according to the adaptive curing compensation parameters and the intelligent curing control strategy, and construct an intelligent collaborative curing light source control model.

[0021] In this embodiment, before designing the intelligent collaborative curing light source control model, requirement analysis is first carried out to determine the functions and performance indicators that the model needs to meet. It mainly includes the dynamic adjustment of light source intensity, the optimization of curing time, and the real-time monitoring of curing effect. Determine the parameters to be considered, such as light source intensity, curing time, temperature and humidity, light uniformity, etc. These parameters will be used as the input variables of the model. Record the results of requirement analysis to ensure that the model can adapt to the changes in the actual curing process. Design the basic structure of the model, usually adopting a hierarchical control framework, including a data acquisition layer, a control layer, and an execution layer. The data acquisition layer is responsible for real-time monitoring of various parameters during the curing process, the control layer is responsible for making decisions according to the set algorithm, and the execution layer is responsible for specific light source adjustment. Determine the interaction relationships between the layers in the model, such as how to transfer the output of data acquisition to the control layer for processing, and how to transfer the decision results of the control layer to the execution layer for actual operation. According to the requirement analysis and model structure, select a suitable control algorithm. Commonly used algorithms include PID control, fuzzy control, and neural network control, etc. The selected algorithm should be able to achieve adaptive adjustment and be optimized according to real-time feedback. Record the selected algorithm and its parameter settings. For example, if PID control is selected, set the initial values of the proportional gain, integral gain, and derivative gain to ensure the stability and response speed of the control. According to the adaptive curing compensation parameters, set the initial parameters of the model. These parameters should include the initial value of light source intensity, the initial setting of curing time, and the compensation values for each region. For example, set the light source intensity in the deviation region to 400 mW / cm² and the curing time to 30 seconds to ensure that the model can adapt to the actual curing requirements in the initial stage. During the curing process, monitor the changes of various parameters in real time, especially pay attention to the feedback of light source intensity and curing effect. Continuously collect data through sensors and record information such as real-time light intensity, curing uniformity, and curing status. If the curing effect is not ideal (for example, the curing uniformity is less than 0.05), adjust the model according to the feedback information. Record the feedback information and the adjustment process for subsequent analysis and optimization. According to the real-time feedback data, iteratively optimize the model parameters. Adjust the light source intensity and curing time to adapt to the new curing requirements. For example, if the uniformity in a certain region is insufficient, the light source intensity in this region can be increased or the curing time can be extended. Record the parameter settings and curing effect evaluation after each optimization to form a complete model optimization record. Ensure that each adjustment is based on scientific data analysis and feedback. Integrate each module into a complete control model, including data acquisition, control algorithm, and execution mechanism. Ensure good connection between each module and smooth data flow. Verify the effectiveness of the model in the experimental environment and evaluate the performance of the model through the actual curing process. Record the performance of the model under different conditions to ensure that it can adapt to various curing requirements.

[0022] In this embodiment, refer to Figure 2, which is a schematic diagram of the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: Obtain the ambient light sensing parameters of the material to be cured; Step S12: Calculate the ambient global light intensity of the ambient light sensing parameters of the material to be cured, and extract the ambient surrounding light intensity; Step S13: Identify the ambient light distribution data of the ambient light sensing parameters; Step S14: Perform light intensity spatio-temporal distribution analysis on the ambient surrounding light intensity and the ambient light distribution data to obtain the spatio-temporal distribution law of the curing ambient light intensity; Step S15: Fit the dynamic light intensity distribution change according to the spatio-temporal distribution law of the curing ambient light intensity, and construct a spatio-temporal distribution change diagram of the curing ambient light intensity.

[0023] In this embodiment, an appropriate ambient light sensor is selected. Usually, a photoresistor, a photodiode or a light sensor module (such as TSL2561) is adopted. These sensors can monitor the ambient light intensity in real time and provide accurate light data. Install the sensor around the curing equipment to ensure that it can capture the light changes during the entire curing process. The sensor should be installed at a certain distance from the material to be cured to avoid the influence of direct light sources. Configure appropriate measurement parameters according to the characteristics of the sensor, such as sampling frequency, sensitivity and response time. Usually, the sampling frequency is set to 1 second to ensure that light changes can be captured in real time. Calibrate the sensor to ensure that the output light intensity value is accurate. A standard light source can be used for calibration, record the relationship between the output value of the sensor and the standard value, and correct any deviations. Before curing the material to be cured, start the sensor and begin data collection, recording the changes in ambient light intensity. The data should include the real-time value of light intensity (such as lx), as well as the acquisition timestamp for subsequent analysis. Set the data recording period (such as recording once per second) to ensure that sufficient light data is obtained for subsequent analysis. The total recording duration can be set to several hours to cover the light changes in different time periods. Preprocess the collected ambient light data to remove outliers and noise. A moving average filter or a median filter can be used to smooth the data to ensure the accuracy of the analysis results. Record the parameters used during the preprocessing, such as the filter window size (such as 5 seconds), so that the same processing method can be reproduced during subsequent analysis. Select an appropriate light intensity calculation method. Usually, the integral method or the weighted average method is used to calculate the global light intensity of the environment. The global light intensity calculation can be achieved by weighted summation of the measurement values of each sensor. Set the weight parameters to ensure that the influence of the distance between the sensor and the material to be cured on the light intensity is reasonably reflected. The inverse weight method can be used for weighting according to the distance between the sensor and the material. Calculate the global light intensity for the preprocessed data to obtain the overall intensity value of the ambient light. Record the calculation result. For example, the total light intensity is 500 lx, indicating the intensity level of the ambient light during the entire curing process. Correlate the calculation result with environmental factors (such as weather, time) for an in-depth understanding of the influence of ambient light on the curing process. Identify and record the distribution data of ambient light according to the output of the sensor. These data should include the light intensity values in various directions and positions to construct a spatial distribution model of ambient light. By arranging sensors at different positions, record the light intensity values at different positions. For example, multiple sensors can be set around the curing area to obtain omnidirectional light distribution data. Use the collected light intensity data to construct a model of ambient light distribution. Interpolation methods (such as Kriging interpolation or linear interpolation) can be used to estimate the light intensity values in unmeasured areas. Record the parameters and methods used during model construction, such as the selection of interpolation algorithm and calculation steps, for subsequent verification and repeated experiments.

[0024] Analyze the constructed light distribution model to identify high and low regions of light intensity, as well as potential light source positions. Heat map visualization techniques can be used to display the light distribution in graphical form for intuitive analysis. Record the analysis results. For example, it is identified that the light intensity in the main light source area is 700 lx, while the light intensity in the shadow area is only 200 lx, showing an obvious difference in light distribution. Integrate the ambient light intensity and light distribution data around the environment to construct a spatio-temporal distribution database. Ensure that the data includes timestamp, light intensity, and spatial position information for comprehensive analysis. Record the parameter settings during the integration process, such as the time interval (e.g., every minute) and the number of sampling points for spatial distribution (e.g., 10), to ensure the comprehensiveness of the data. Select suitable spatio-temporal analysis methods. Time series analysis and spatial statistical models (such as Kriging model or spatial autocorrelation analysis) can be used to analyze the spatio-temporal variation law of light intensity. Set the analysis parameters, such as the time window (e.g., 1 hour) and the spatial analysis scale, to ensure the reliability of the analysis results. Analyze the integrated spatio-temporal data to identify the variation law of light intensity. For example, analyze the light intensity change trend in different time periods (such as day and night), and find that the average light intensity during the day is 600 lx, while at night it is only 150 lx. Record the results of spatio-temporal distribution analysis, including the specific values and trend charts of light intensity changes, for subsequent fitting of dynamic light intensity distribution changes. Select suitable fitting models. Polynomial regression, curve fitting, or machine learning methods (such as support vector regression) can be used to construct a light intensity distribution change model. Determine the parameter settings for fitting, such as selecting the order of the polynomial (e.g., second or third order) or the training parameters of the machine learning model, to ensure the accuracy of the fitting.

[0025] Fit the light intensity data obtained from spatio-temporal distribution analysis to generate a dynamic light intensity distribution change model. Adjust the model parameters through the least squares method or other optimization algorithms to make the fitting curve as close as possible to the actual data. Record the parameters and results during the fitting process. For example, the root mean square error (RMSE) of the fitting is 5 lx, indicating a good fitting effect. Verify the effectiveness of the fitting model by comparing it with the actual measurement data to evaluate the prediction ability of the model.

[0026] The R² value can be calculated to ensure the interpretability and prediction accuracy of the model. Optimize the model based on the verification results. Adjust the model structure or reselect the fitting method if necessary to ensure that the final model can accurately reflect the changes in the curing ambient light intensity. Select appropriate visualization tools to convert the fitted dynamic light intensity distribution change model into a graphical form. Data visualization software (such as Matplotlib or Origin) can be used to generate visualization charts. Determine the format and style of the charts, such as choosing heat maps, 3D plots, or contour plots, for intuitive display of light intensity changes. Generate a spatio-temporal distribution change map of the curing ambient light intensity based on the fitted model and actual data, ensuring that the map contains detailed information on time, space, and light intensity. Record the parameter settings used when generating the charts, such as the color mapping range (e.g., 0 - 800 lx), to ensure that the charts can clearly reflect the light intensity changes.

[0027] In this embodiment, refer to Figure 3 , which is a schematic diagram of the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: Collect the light source parameters of the UV lamp bead array based on the intelligent signal machine of the light source. Step S22: Identify the light source parameters of each lamp bead for the light source parameters of the UV lamp bead array, and extract the UV light source intensity and wavelength parameters of each UV lamp bead. Step S23: Conduct an evolution of the light intensity change trend based on the spatio-temporal distribution change map of the curing ambient light intensity to obtain the evolution characteristics of the light intensity change trend. Step S24: Identify the light intensity differences in multiple regions for the evolution characteristics of the light intensity change trend to generate the light intensity difference characteristics of different regions. Step S25: Adjust the light source parameters of the corresponding lamp beads for the UV light source intensity and wavelength parameters according to the light intensity difference characteristics of different regions to obtain a differential UV light source parameter adjustment strategy.

[0028] In this embodiment, configure the parameters of the signal machine, such as the sampling frequency (e.g., once per second) and the data recording format, to ensure that the collected data is complete and accurate. Set the total duration of data recording to cover the entire curing process. And ensure that it can work properly. Make necessary electrical connections and light source calibrations to ensure that each lamp bead can work independently and output stable UV light. During the debugging process, record the initial state of each lamp bead, including its position, power, and operating voltage, for subsequent analysis and comparison. Start the light source intelligent signal machine to begin collecting the light source parameters of the UV lamp bead array. The data should include the UV light source intensity (e.g., mW / cm²) and wavelength parameters (e.g., nm) of each lamp bead, as well as the timestamp. Record the collected data to ensure that the data storage format is convenient for subsequent processing. For example, store the light source intensity and wavelength data in CSV format to form a structured data set. Preprocess the collected light source parameters to remove outliers and noise. Statistical methods, such as the mean and standard deviation, can be used to identify and eliminate significantly unreasonable data points. Record the parameter settings during the preprocessing process, such as setting the threshold (e.g., ±10% range of the light intensity), to ensure the accuracy and reliability of the final data. Identify the parameters of each UV lamp bead and extract its UV light source intensity and wavelength parameters. According to the timestamp in the data set, ensure that the parameters of each lamp bead are compared at the same time point. Generate a parameter report for each lamp bead. For example, the UV light source intensity of lamp bead 1 is 150 mW / cm² and the wavelength is 365 nm. Record these parameters for subsequent analysis. Integrate the light source parameters of all lamp beads into a table for subsequent analysis and comparison. The table should include the lamp bead number, light source intensity, wavelength, and other relevant information. Conduct preliminary data analysis to identify the performance differences between each lamp bead. For example, it is found that the light source intensity of some lamp beads is significantly higher than that of other lamp beads, which affects the curing effect. Utilize the spatio-temporal distribution change map of the curing ambient light intensity obtained in the previous steps to ensure that it contains the light intensity data at different time periods and spatial positions. This map should be able to reflect the impact of the change in ambient light on the curing effect. Record the main features in the map, including the high and low light intensity regions, the change trend, and its impact on the curing process, for subsequent analysis. Conduct trend analysis on the spatio-temporal distribution change map of the light intensity to identify the pattern of light intensity change.

[0029] The time series analysis method can be used to analyze the intensity change trend in different time periods. Record the specific parameters of the change, for example, the average light intensity is 500mW / cm² in a certain time period, and 300mW / cm² in another time period, indicating that the light intensity has significant fluctuations. Extract the evolution characteristics from the light intensity change trend and identify the key factors affecting the curing effect, including light source intensity, environmental changes, etc. Statistical analysis methods, such as regression analysis, can be used to quantify the impact of these factors on the change of light intensity. Record the specific values ​​and trends of the evolution characteristics, such as the rate of light intensity change is 50mW / cm² per hour, indicating the dynamic characteristics of light intensity change. Divide the curing area into multiple sub-areas and perform light intensity difference analysis separately. The area can be divided into high light intensity area, medium light intensity area and low light intensity area according to the different light intensity. Record the light intensity value of each area, for example, the average light intensity of the high light intensity area is 600mW / cm², the medium light intensity area is 400mW / cm², and the low light intensity area is 200mW / cm². By comparing the light intensity data of different areas, the light intensity difference characteristics are extracted. Statistical methods can be used to calculate the standard deviation and coefficient of variation of the light intensity of each area to quantify the light intensity difference between areas. Record the specific values ​​of the light intensity difference characteristics, for example, the light intensity difference between the high light intensity area and the low light intensity area is 400mW / cm², indicating that the lighting conditions in different areas are significantly different. According to the light intensity difference characteristics, formulate corresponding UV light source parameter adjustment strategies. Considering the curing requirements of different areas, set different light intensity and wavelength target values. Record the parameter settings of the adjustment strategy, for example, the UV light source intensity in the high light intensity area is set to 600mW / cm², while the low light intensity area is set to 300mW / cm² to meet the curing effect. Adjust the light source intensity and wavelength parameters of the UV lamp beads to ensure that the output of each lamp bead can adapt to the light intensity requirements of its area. The change of light source intensity can be achieved by adjusting the current and operating frequency. Record the adjustment process of each lamp bead, for example, adjust the output intensity of lamp bead 1 from 150mW / cm² to 600mW / cm², and adjust its wavelength to 365nm to improve the curing effect. After the adjustment is completed, verify the effect to check whether the curing effect meets the expectations. The hardness and adhesion of the cured material can be measured experimentally for evaluation. Record the verification results. For example, if the hardness value of the cured material is 80 Shore D, it indicates that the curing effect is good. If necessary, further optimize the adjustment strategy based on the verification results to achieve the best curing effect.

[0030] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: performing real-time curing irradiation on the material to be cured according to the differentiated UV light source parameter adjustment strategy, and collecting real-time curing irradiation spectrum monitoring images; Step S32: Identify the position of the material to be cured based on the cured irradiation spectrum monitoring image, and perform multi-spectral response feature analysis to extract the multi-spectral response features of the material to be cured; Step S33: Analyze the optical changes during the curing process based on the multi-spectral response features of the material to be cured to obtain the optical change information of the material to be cured; Step S34: Perform multi-period sensitivity analysis on the optical change information of the material to be cured to obtain the UV light source sensitivity at different irradiation times; Step S35: Evaluate the curing process matching degree based on the UV light source sensitivity at different irradiation times to generate the curing process matching degree; Step S36: Adjust the curing light source intensity and curing time according to the curing process matching degree to construct an intelligent curing control strategy.

[0031] In this embodiment, according to the differential UV light source parameters determined in the previous step, configure the power, wavelength, and irradiation time of the UV lamp. Ensure that the output intensity and wavelength of each lamp bead can meet the curing requirements of their respective areas. Record the set parameters. For example, the output intensity of the UV light source is set to 600 mW / cm², the wavelength is set to 365 nm, and the irradiation time is 30 seconds to ensure the effectiveness of the curing process. Establish a real-time spectral monitoring system equipped with a high-sensitivity spectrometer that can record the spectral response of the material during the curing process in real time. Ensure that the spectrometer can cover the UV band and its adjacent visible light band. Calibrate the spectrometer to ensure the accuracy of the output spectral data. Use a standard light source for calibration and record the calibration results for subsequent analysis. While performing the curing irradiation, start the spectral monitoring system to collect the spectral images of the material to be cured in real time. The monitoring data should include timestamp, spectral intensity, and wavelength information to form a structured data set. Record the data acquisition frequency, such as collecting spectral data once per second, to ensure that spectral changes during the curing process can be captured. Select a suitable image processing algorithm to identify the position of the material to be cured. Computer vision techniques, such as edge detection and contour extraction, can be used to identify the curing area. Set the algorithm parameters, such as the threshold setting for Canny edge detection, to ensure that the boundary of the material can be accurately identified. According to the identified material position, extract its spectral response characteristics. Analyze the changes in the spectral images during the curing process, identify the key wavelengths and response intensities, and form a multi-spectral response feature set. Record the extracted feature parameters. For example, the spectral intensity at 365 nm is 1200 mW, indicating a strong optical response of the material at this wavelength. Analyze the extracted multi-spectral response characteristics to understand the response characteristics of the material to be cured at different wavelengths. Methods such as principal component analysis (PCA) can be used for dimensionality reduction to extract the main spectral components. Record the analysis results to identify the absorption and reflection characteristics of the material at specific wavelengths. For example, in the 300 - 400 nm band, the reflectivity of the material gradually decreases, indicating the start of the curing reaction. Select a suitable optical model to analyze the optical changes of the material to be cured. An optical property-based model, such as the Beer-Lambert law, can be used to analyze the absorption and transmission characteristics of the material to light. Determine the model parameters, such as the absorption coefficient and scattering coefficient of the material, for use in subsequent analysis. According to the extracted spectral response characteristics, analyze the optical changes of the material during the curing process. Record the optical property changes at different time points, such as light absorption rate, transmittance, and reflectivity. Record the specific numerical values of the optical changes. For example, within 5 seconds after the start of curing, the optical absorption rate of the material increases from 30% to 70%, indicating that the curing reaction gradually accelerates. Summarize the optical change information to form an optical change report of the material to be cured. The report should include the time series of optical property changes and key parameters for subsequent analysis and optimization. Record the analysis results, such as the rate and trend of optical changes, to provide a basis for optimizing the curing process.Select a suitable sensitivity analysis method, usually variance sensitivity analysis or local sensitivity analysis, to evaluate the impact of UV light sources at different irradiation times on the curing process. Determine the parameter settings for the analysis, such as the irradiation time points for analysis (e.g., 0 s, 5 s, 10 s, etc.), in order to observe the optical changes at different times. Conduct a sensitivity analysis on the optical changes at different time points during the curing process to evaluate the influence of UV light source intensity and irradiation time on the curing effect. Record the light absorption, transmission, and reflection characteristics at each time point. Record the analysis results, such as the change in absorption rate at different time points, and find that the influence of light source intensity on the curing effect is significant within the first 10 seconds and then tends to stabilize. Summarize the sensitivity analysis results to form a report, recording the sensitivity characteristics of the UV light source at different irradiation times. The report should include key parameters and trend charts for subsequent decision-making. For example, record that at an irradiation time of 10 s, the change amplitude of the material's absorption rate is the largest, while the change after 30 s is relatively stable. Select a suitable matching degree evaluation method, usually the weighted scoring method or the fuzzy mathematics method, to evaluate the adaptability of different curing processes at different irradiation times. Determine the parameters for evaluation, such as irradiation time, light source intensity, and material properties, for comprehensive evaluation. Based on the results of the sensitivity analysis, evaluate the matching degree of the curing processes at different irradiation times. Consider the light source intensity and curing effect comprehensively and calculate the matching degree score. Record the evaluation results. For example, at an irradiation time of 30 s, the score is 85 points, indicating good adaptability of the process to the material. Summarize the evaluation results of the curing process matching degree to form a report, recording the evaluation scores and their corresponding curing process parameters. Propose optimization suggestions based on the evaluation results to improve the curing effect. For example, it is recommended to appropriately increase the light source intensity when the irradiation time reaches 20 s to obtain a better curing effect. Based on the evaluation results of the curing process matching degree, formulate corresponding adjustment strategies. Set different light source intensities and irradiation times to achieve the best curing effect. Record the specific parameters of the adjustment strategy. For example, in the process with a high matching degree, the light source intensity is set to 700 mW / cm² and the irradiation time is set to 25 s. Adjust the light source intensity and irradiation time of the UV lamp to ensure that it can comply with the newly formulated intelligent curing control strategy. Monitor the curing effect after adjustment in real time. Record the parameter changes during the adjustment process, such as the light source intensity being increased from 600 mW / cm² to 700 mW / cm², and observe the changes in the optical properties of the cured material. Establish an intelligent curing control system to integrate data on light source intensity, irradiation time, and optical changes, and adjust the curing parameters in real time. Ensure that the system can automatically optimize the curing process based on real-time feedback. Record the operating effect of the intelligent control system. For example, after adjustment, the hardness value of the cured material is increased to 90 Shore D, indicating a significant improvement in the curing effect.

[0032] In this embodiment, the specific steps of identifying the position of the material to be cured according to the cured irradiation spectrum monitoring image, performing multi-spectral response feature analysis, and extracting the multi-spectral response features of the material to be cured are as follows: Perform target visual positioning based on the cured irradiation spectrum monitoring image and mark the material to be cured; Calculate the spatial coordinates of the material to be cured; Perform spectral change analysis at different time points according to the spatial coordinates, and extract the multi-time point spectral change features of the material to be cured; Calculate the reflectivity of the material to the UV light source based on the multi-time point spectral change features to generate the reflectivity of the UV light source; Analyze the surface color temperature change features of the multi-time point spectral change features; Perform multi-spectral response feature analysis according to the UV light source reflectivity and the surface color temperature change features, and extract the multi-spectral response features of the material to be cured.

[0033] In this embodiment, suitable image processing tools and algorithms are selected, such as the OpenCV or the MATLAB Image Processing Toolbox. These tools can effectively process spectral monitoring images and extract the characteristics of the material to be cured. Determine the processing flow, including steps such as denoising, enhancing contrast, and edge detection, to improve the recognizability of the material in the image. Use image processing algorithms for target visual positioning. First, preprocess the monitoring image, use Gaussian filtering to remove noise, and apply an edge detection algorithm (such as the Canny algorithm) to extract the edges of the material. Identify the position of the material to be cured through a contour detection algorithm and record its coordinate information. Set the threshold parameters for contour recognition to ensure that the boundaries of the material are accurately captured. Mark the position of the material to be cured in the image and record its coordinates (x, y). For example, mark the center coordinates of a specific material as (150, 200) and store the marking information in a dataset for subsequent analysis. Record the processing results and image parameters, such as the image resolution (e.g., 1024x768 pixels) and the processing time, to ensure the repeatability of subsequent operations. Determine the reference point and the axis directions of the spatial coordinate system. Usually, select the upper left corner of the image as the origin and define the coordinate system with the x-axis horizontal to the right and the y-axis vertical downward. Record the parameter settings of the coordinate system to ensure that the calculation of spatial coordinates matches the actual position of the material. Use a depth sensor (such as a laser rangefinder or a stereo vision camera) to obtain the depth information of the material to be cured, and calculate the three-dimensional spatial coordinates (x, y, z) of the material in combination with the image coordinates. Collect the depth data, ensuring the accuracy of the depth information for each coordinate point. For example, record the depth value of the material as 150 mm, and calculate the spatial coordinates as (150, 200, 150) in combination with the coordinate information from the previous step. Collect the spectral monitoring data of the material to be cured at different time points. Ensure that the spectral data at each time point is associated with the spatial coordinates to form a time series dataset. Record the settings of the time points, such as performing spectral acquisition at time points of 0 seconds, 5 seconds, 10 seconds, and 30 seconds, etc., to ensure coverage of different stages of the curing process. Select a suitable analysis method, usually using statistical analysis or machine learning methods (such as principal component analysis PCA) to extract the spectral change characteristics. Set the parameters of the analysis, such as selecting the number of features and the analysis dimension. Record the parameter settings during the analysis to ensure the repeatability and reliability of the results. Analyze the spectral data at different time points to extract the multi-time point spectral change characteristics of the material to be cured. Record the spectral intensity and wavelength at each time point to form a feature set. For example, record the spectral intensity at 5 seconds as 800 mW / cm² and at 10 seconds as 1000 mW / cm², analyze its change trend, and obtain the specific characteristics of the spectral response. Select a suitable reflectivity calculation model, usually based on the law of light reflection and spectral characteristics. For example, use the reflectivity formula: Reflectivity = Reflected light intensity / Incident light intensity. Determine the parameter settings of the model and record the reference value of the incident light intensity for calculating the reflectivity.Based on the spectral data, calculate the UV light source reflectivity of the material to be cured. For each time point, record the incident light intensity and the reflected light intensity, and calculate the reflectivity. For example, if the incident light intensity is 1200 mW / cm² at 10 seconds and the reflected light intensity is 300 mW / cm², the calculated reflectivity is 25%. Record the calculated UV light source reflectivity in the data set, including the reflectivity changes at different time points. Analyze the trend of the reflectivity over time to evaluate the reflection characteristics of the material. Record the analysis results, such as the reflectivity is 20% at 5 seconds and increases to 30% at 30 seconds, indicating the spectral response changes during the curing process. Select a suitable color temperature calculation model, usually using the blackbody radiation theory or the CIE standard for color temperature calculation. Calculate the effective wavelength based on the spectral data and deduce the color temperature. Determine the calculation parameter settings, such as the selected wavelength range (e.g., 400 - 700 nm), to ensure the accuracy of the results. Calculate the surface color temperature of the material to be cured according to the spectral change characteristics at multiple time points. At each time point, record the spectral intensity and the corresponding color temperature value. For example, if the color temperature is 5500 K at 5 seconds and 6000 K at 10 seconds, analyze its trend over time. Select a suitable analysis method, such as spectral filtering and feature extraction methods, to extract the multi-spectral response characteristics of the material to be cured. Determine the analysis parameter settings, such as the selected wavelength range and the feature extraction algorithm (e.g., PCA or LDA). Use the obtained UV light source reflectivity and color temperature change data to analyze the multi-spectral response characteristics of the material to be cured. Record the reflectivity and color temperature characteristics at different wavelengths to form a multi-spectral response data set. For example, record the reflectivity at 400 nm is 25% and at 600 nm is 40%, and analyze its relationship with the curing effect. Organize the extracted multi-spectral response characteristics to form a report, recording the key analysis results and feature parameters. Record the relationship between the response characteristics and the curing effect, such as the reflectivity at different wavelengths is positively correlated with the curing degree of the material, providing a basis for optimizing the curing process.

[0034] In this embodiment, step S4 includes the following steps: Based on the intelligent curing control strategy, perform real-time light source curing process analysis on the real-time curing irradiation spectral monitoring image, and extract multi-curing stage response data; Perform analysis on the temperature change distribution of the material for the multi-curing stage response data to generate the temperature fluctuation distribution characteristics of the material; Perform analysis on the surface texture color trend of the material according to the multi-curing stage response data, and extract the surface texture color trend characteristics; Based on the temperature fluctuation distribution characteristics of the material and the surface texture color trend characteristics, perform real-time curing state mining to obtain the real-time curing state; Define the time window length; based on the time window length, perform a rolling prediction of the future curing trend of the real-time curing state, and generate curing trend prediction diagrams of materials for multiple time windows.

[0035] In this embodiment, a real-time spectral monitoring system is configured to ensure that it can continuously collect spectral data during the curing process. The system should include a highly sensitive spectral detector that can cover the UV band and its adjacent visible light band. Set monitoring parameters, such as the sampling frequency (collecting a spectral image once per second), and record the start time and end time of the monitoring for subsequent data analysis. During the curing process, use the spectral monitoring system to collect spectral data of each curing stage in real time. The duration of each curing stage can be set to 10 seconds, 20 seconds, and 30 seconds to capture spectral changes at different stages. Record the spectral image of each stage and its corresponding timestamp, and ensure the integrity and accuracy of the data. The data of each stage should include spectral intensity and characteristic wavelength information for subsequent analysis. Analyze the collected spectral data to extract the response data of each curing stage. Feature extraction algorithms can be used to identify key change points in the spectral diagram, such as absorption peaks and reflection peaks. For example, record that the spectral response data at 10 seconds is a wavelength of 370 nm and the light intensity value is 800 mW / cm², and observe that the light intensity value increases to 1000 mW / cm² at 20 seconds, indicating spectral changes during the curing process. Equip with a temperature sensor (such as a thermocouple or an infrared thermometer) to monitor the temperature change of the material in real time. Ensure that the sensor can accurately measure the temperature of the cured material surface, and record the timestamp of the data synchronously with the spectral data. Set the temperature sampling frequency, such as recording temperature data once per second, to ensure the time consistency with the spectral data. During the curing process, collect the temperature change data of the material surface in real time. Record the temperature value of each curing stage and ensure the integrity of the data. For example, measure the temperature at 25°C at 10 seconds and the temperature rises to 30°C at 20 seconds. Record the specific values of the temperature change for subsequent analysis. For example, record data points including time, temperature, and spectral response to form a comprehensive data set. Conduct statistical analysis on the temperature change data to generate the temperature fluctuation distribution characteristics of the material. Visualization tools (such as heat maps) can be used to display the temperature distribution at different stages. Record the analysis results. For example, during different curing stages, the temperature fluctuation range is from 25°C to 30°C, indicating the temperature stability and change trend of the material during the curing process. Equip with a high-resolution camera or image sensor to conduct real-time monitoring of the surface texture and color of the material. Ensure that the camera can take high-quality images during the curing process to capture surface details. Set the parameters of the camera, such as white balance, exposure time, and ISO sensitivity, to ensure the image quality. At different curing stages, use the camera to take real-time images of the material surface. Record the image at each time point and ensure that the timestamp of the image is synchronized with the spectral and temperature data. For example, the image taken at 10 seconds is recorded as "image_10s.jpg", the image taken at 20 seconds is "image_20s.jpg", and record the shooting time of each image. Conduct color analysis on the collected images to extract the color characteristics and change trends of the surface texture.Color space conversion (such as RGB to HSV) can be used to analyze the change of surface color. Record the analysis results. For example, the average hue of the surface color at 10 seconds is Hue = 200°, and it rises to Hue = 210° at 20 seconds, indicating the change of surface color with the curing process. Select a suitable algorithm for real-time curing state mining. A rule-based method or a machine learning algorithm (such as Support Vector Machine SVM) can be used to analyze the relationship between temperature and color features and the curing state. Define state indicators, such as temperature stability, color change rate, etc., to facilitate the evaluation of the real-time curing state. Analyze the curing state of the material in real time according to the temperature fluctuation distribution characteristics and the surface texture color trend characteristics. Record the state indicators at each time point and judge the current curing state of the material. For example, if the temperature fluctuation is less than 2°C and the color change rate is within the acceptable range, mark the current curing state as "normal". Record the real-time curing state in the dataset, including the state, temperature, and color features at each time point, for subsequent analysis and optimization. For example, record that the curing state is "normal" at 10 seconds and the state changes to "abnormal" at 20 seconds to adjust the curing parameters in time. Define a suitable time window length according to the characteristics of the curing process. For example, set the time window to 5 seconds for short-term prediction of the real-time state. Record the parameter settings of the time window to ensure the accuracy and consistency of subsequent predictions. Select a suitable prediction model, such as a time series prediction model (such as ARIMA) or a machine learning regression model (such as Random Forest), for predicting the future curing state. Configure the model parameters, such as the selected feature variables (temperature, color change, etc.), to ensure that the model can effectively capture the change trend of the curing state. Based on the defined time window, perform a rolling prediction of the future curing state for the real-time curing state. Analyze the state characteristics within each time window to generate prediction results for multiple time windows. Record the prediction results, such as the curing state predictions within the next 5 seconds, 10 seconds, and 15 seconds, and generate a corresponding curing situation prediction chart. Visualize the prediction results to generate a curing situation prediction chart, showing the change of the material curing state within different time windows. A line chart or a heat map can be used to display the prediction results.

[0036] In this embodiment, step S5 includes the following steps: Obtain the preset curing log and perform final material curing effect identification to generate the final material curing effect; Calculate the curing uniformity for the material curing situation prediction charts of multiple time windows to generate a predicted value of the material curing uniformity; Detect the curing deviation area for the predicted value of the material curing uniformity according to the final material curing effect and mark the curing deviation area; Perform in-depth analysis of the deviation trend for the curing deviation area to generate area deviation trend characteristics; Perform adaptive solidification deviation compensation calculation on the regional deviation trend characteristics to obtain adaptive solidification compensation parameters.

[0037] In this embodiment, preset curing logs are extracted from the curing control system. The logs should contain key parameters for each curing stage, such as UV light source intensity, irradiation time, temperature change, and monitoring data. Record the structure of the logs, including timestamps, curing parameters, and corresponding curing states, to ensure data integrity and traceability. Select a suitable method for identifying the curing effect, usually physical property tests (such as hardness tests, adhesion tests) or spectroscopic analysis methods. A spectrophotometer can be used to evaluate the optical properties of the cured material. Ensure quantitative analysis of the curing effect and set specific evaluation criteria. For example, the hardness test requires reaching a certain value (such as above 80 Shore D) to indicate good curing effect. Record and analyze the results according to the identified method to generate a final report on the curing effect of the material. The report should include test results, analysis conclusions, and curing effect evaluations. For example, record the final curing effect as "good", with a hardness value of 85 Shore D, indicating that the curing process is effective and the material properties meet the requirements. Select a suitable method for calculating the curing uniformity. Usually, statistical analysis methods such as standard deviation and root mean square error (RMSE) are used to evaluate the curing uniformity within different time windows. Determine the parameter settings for the calculation, such as the selected time window length (such as 5 seconds or 10 seconds), to ensure the accuracy of the analysis. Analyze the curing trend prediction diagrams for multiple time windows, collect the curing parameters (such as light intensity, temperature, etc.) within each time window, and calculate their uniformity. Record the calculation results. For example, the predicted uniformity value within a certain time window is 0.05, indicating that the curing effect within this time window is relatively uniform. Set the detection criteria for the curing deviation area according to the final curing effect of the material. The threshold method can be used to set the allowable uniformity range (such as uniformity less than 0.05 is normal, and greater than 0.05 is a deviation). Record the set criteria to ensure the consistency of subsequent detections. Detect the predicted values of the curing uniformity of the material and identify the areas that exceed the set threshold. Image processing techniques can be used to mark the deviation areas on the curing trend prediction diagram. For example, if the uniformity value in a certain area is 0.07, mark this area as the "curing deviation area". Select a suitable method for analyzing the deviation trend. Time series analysis or regression analysis can be used to study the change trend of the curing deviation area. Determine the parameter settings for the analysis, such as the selected time window and sampling frequency, to ensure the accuracy of the analysis. Conduct in-depth analysis of the uniformity values in the curing deviation area, record the deviation situation at each time point, and analyze its change trend. For example, record the change in the uniformity of the deviation area at consecutive time points and analyze its rising or falling trend. Develop an adaptive curing compensation strategy based on the deviation trend characteristics. The method of dynamically adjusting the light source intensity and irradiation time can be used to compensate for the insufficient curing in the deviation area. Record the parameter settings of the compensation strategy, such as the adjustment amplitude of the light source intensity (such as increasing by 10%) and the extension of the irradiation time (such as increasing by 2 seconds). Calculate the adaptive curing compensation parameters according to the characteristics of the deviation area and implement the corresponding adjustments.Monitor the curing effect after compensation to ensure that the expected uniformity can be achieved. Record the calculation process of the compensation parameters. For example, within the deviation area, the light source intensity is increased from 600 mW / cm² to 660 mW / cm². After implementing the compensation, verify the curing effect to ensure that the compensation measures are effective. The compensation effect can be evaluated through subsequent uniformity tests. Record the verification results. For example, the uniformity in the deviation area after compensation is reduced to 0.04, indicating that the compensation measures are successful and the curing uniformity is improved.

[0038] In this embodiment, step S6 includes the following steps: Perform fine-tuning of the UV light source power output compensation for the light source intelligent signal machine according to the adaptive curing compensation parameters to obtain the output compensated light source power; Perform real-time output power optimization on the output compensated light source power to generate the optimized light source output power; Based on the optimized light source output power and the intelligent curing control strategy, perform iterative collaborative control optimization to construct an intelligent collaborative curing light source control model; Upload the intelligent collaborative curing light source control model to the light source intelligent signal machine to execute the UV curing control operation.

[0039] In this embodiment, the adaptive curing compensation parameters are obtained from the previous step, and their influence on the UV light source power output is analyzed. The specific compensation parameters include the increase amplitude of the light source intensity, the extension of the irradiation time, and other related factors. Record the specific values of the compensation parameters. For example, the light source intensity increases from 600 mW / cm² to 660 mW / cm² to ensure the basis for subsequent adjustments. According to the compensation parameters, finely adjust the UV light source power output of the light source intelligent signal machine. Ensure that the power output of each lamp bead can be accurately adjusted to match the compensation requirements. For example, set the output of the light source power to be finely adjusted from the original 600 mW / cm² to 660 mW / cm². During the adjustment process, the power output should be monitored in real time to ensure the accuracy of the adjustment. After completing the fine adjustment of the power output, record the new output compensation light source power to form a complete output parameter record. Include the adjusted power value, adjustment time, and relevant environmental parameters. For example, record the output compensation light source power as 660 mW / cm², and the adjustment time as 10:05, and ensure that these data can be used for subsequent analysis and verification. Determine the strategy for optimizing the real-time output power. Usually, a feedback control mechanism is adopted to evaluate the influence of the light source output power on the curing effect in real time. Set the optimization parameters, such as the allowable power fluctuation range (e.g., ±5%), to ensure the stability and safety during the optimization process. According to the real-time monitored data, perform optimization adjustments on the output compensation light source power. The PID control algorithm or fuzzy control algorithm can be used to ensure that the power output is within the preset range. For example, if it is monitored that the current light source power is 660 mW / cm², but the curing effect is not ideal, the system automatically adjusts the power to 630 mW / cm² to optimize the power output in real time. According to the optimized light source output power and the intelligent curing control strategy, design an iterative collaborative control model. The model should include the collaborative control of multiple variables such as light source power, irradiation time, and temperature monitoring. Determine the structure and algorithm of the model. For example, use the state space model or multivariable control algorithm to ensure that the model can effectively achieve collaborative control. Set the parameters of the model, including the gain coefficients, response time, and other control parameters of the PID controller, to ensure the stability and response speed of the model. For example, set the proportional gain of the PID controller to 1.5, the integral gain to 0.1, and the derivative gain to 0.05 to optimize the response performance. Test the performance of the collaborative control model in the experimental environment. By simulating the curing process, verify the effectiveness and stability of the model. Record the test results. For example, the curing uniformity reaches 0.02, and the curing effect is good, and complete the verification and optimization of the model. Prepare to upload the tested and verified intelligent collaborative curing light source control model to the light source intelligent signal machine. Ensure that the model file format is compatible with the signal machine system. Record the parameter settings and file name during the upload process to ensure that the model can be correctly identified and loaded in the signal machine. Through the control interface of the signal machine, perform the model upload operation. Ensure a stable connection with the signal machine during the upload process to avoid data loss or upload failure.Record the time and status of the upload completion to ensure that the system can start a new control model normally. Start the intelligent signal machine for the light source and execute the UV curing control operation. Monitor the real-time data during the curing process to ensure that the system operates according to the new intelligent collaborative control model. Record the key parameters during the curing process, such as light source power, irradiation time, and temperature changes, for subsequent analysis and optimization.

[0040] In this embodiment, a control system for an intelligent signal machine for a light source is provided, which is used to execute the method for controlling an intelligent signal machine for a light source as described above, and includes: A light intensity spatio-temporal distribution module, which is used to obtain the ambient light sensing parameters of the material to be cured, perform light intensity spatio-temporal distribution analysis and fitting of dynamic light intensity distribution changes, and construct a spatio-temporal distribution change diagram of the light intensity in the curing environment; A differential adjustment module, which is used to collect the light source parameters of the UV lamp bead array; adjust the light source parameters of the corresponding lamp beads according to the spatio-temporal distribution change diagram of the light intensity in the curing environment to obtain a differential UV light source parameter adjustment strategy; A curing parameter adjustment module, which is used to perform real-time curing irradiation on the material to be cured according to the differential UV light source parameter adjustment strategy, and adjust the curing light source intensity and curing time to construct an intelligent curing control strategy; A curing situation prediction module, which is used to perform real-time curing state mining and rolling prediction of the curing situation in the future period based on the intelligent curing control strategy, and generate a curing situation prediction diagram of the material for multiple time windows; An adaptive compensation module, which is used to obtain a preset curing log, detect the curing deviation area of the material curing situation prediction diagram, and perform deviation compensation calculation to obtain an adaptive curing compensation parameter; A collaborative control optimization module, which is used to perform iterative collaborative control optimization according to the adaptive curing compensation parameter and the intelligent curing control strategy, and construct an intelligent collaborative curing light source control model.

[0041] By collecting environmental light data (such as light intensity, light source angle, wavelength, etc.), the present invention can comprehensively understand the light conditions of the environment where the material to be cured is located. Since the light intensity and distribution mode are different in different production environments, this module provides an accurate modeling of the spatio-temporal changes of environmental light intensity, which helps the subsequent adjustment of the light source. Through the analysis of the spatio-temporal distribution of light intensity, anomalies in the light environment (for example, the light intensity in some areas is too low or too high) can be found, providing data support for the subsequent adjustment. The fitting of dynamic light intensity changes can monitor environmental changes in real time, give early warnings and provide a basis for system adjustment. The spatio-temporal distribution change map of light intensity provides dynamic environmental adaptation data, enabling the system to automatically adjust the control strategy according to environmental changes, so as to ensure that the UV light source can work continuously and stably under variable environmental conditions and ensure the consistency of curing quality. By collecting and analyzing the light source parameters (such as power, radiation angle, wavelength, etc.) of each UV lamp bead array in real time, the output of each lamp bead can be adjusted individually according to the environmental light data. The curing requirements of each area are different, and the differential light source parameter adjustment strategy can avoid unnecessary energy waste and ensure uniform light intensity distribution during the curing process. The environmental light intensity change map provides real-time light intensity and distribution information of different areas, and the differential adjustment module can adjust the light source according to this information to ensure that the light source power is reduced in high-light areas and increased in low-light areas, thus avoiding problems such as over-curing or insufficient curing. According to the light change map, the system can intelligently respond to the needs of different curing areas and perform dynamic adjustment, so as to ensure the stability and efficiency of the curing process and improve the production quality. It can monitor the light source intensity and curing time during the curing process in real time and make flexible adjustments according to environmental light changes and the curing requirements of the material. Real-time adjustment can ensure the optimization of the curing process and improve the curing quality of the material. By establishing an intelligent curing control strategy, the system can automatically adjust the curing parameters (such as light source intensity, exposure time, etc.) at different time periods to cope with the changes in environmental light intensity and different reactions of the curing material, realizing the intelligent coordination of the light source and the curing process. This module can adjust the exposure time and light source intensity according to the real-time reaction of the material to ensure that the curing process is neither excessive nor insufficient, thus avoiding production defects caused by insufficient curing or over-curing. By mining the curing state in real time, anomalies or deviations in the curing process can be identified in time and adjustments can be made quickly. In addition, the real-time feedback of the curing state provides data support for subsequent optimization. By rolling and predicting the curing trend within multiple time windows, the system can discover potential problems in advance and make adjustments. For example, predicting the over-cured area or under-cured area that appears, so as to adjust the curing strategy in time. Through the future prediction of the curing trend, the scheduling and arrangement in the production process can be optimized in advance, avoiding unnecessary downtime or adjustment, improving production efficiency and reducing the stagnation in the production process.By analyzing the curing logs and comparing them with the curing trend prediction chart, the system can timely detect the deviation areas during the curing process, such as over - curing or under - curing in certain areas, ensuring precise control of the entire curing process. Based on the deviation areas, the system can dynamically calculate compensation parameters and automatically adjust the light source intensity or exposure time. Through real - time feedback and compensation, overall quality problems caused by local deviations are avoided. Adaptive compensation can significantly improve the consistency of curing quality, ensuring that the same curing effect can be obtained in different areas of the product. The compensation strategy can optimize the curing process and improve the product quality control level. By continuously iteratively optimizing the light source control strategy, parameters such as light source power and exposure time are gradually improved to better adapt to changes in the production environment and materials. This optimization process can enhance the curing effect based on historical data and real - time feedback. By coordinately controlling different light sources, the module can ensure that the curing effects in different areas are consistent, avoiding quality differences caused by uneven light sources or insufficient power. Co - optimization also helps to improve the energy efficiency and stability of the entire production process. This module combines adaptive compensation and intelligent control strategies to ensure the coordinated operation of the light source and the curing process, thus guaranteeing the efficiency and stability of the curing process. The optimized control strategy reduces the need for manual intervention and further improves the automation level.

[0042] Therefore, from any perspective, the embodiments should be regarded as exemplary and non - restrictive. The scope of the present invention is defined by the appended claims rather than the above description. Thus, all changes falling within the meaning and scope of the equivalent elements of the application documents are intended to be encompassed within the present invention.

[0043] As described above, these are only specific embodiments of the present invention, enabling those skilled in the art to understand or implement the present invention. Various modifications to these embodiments will be obvious to those skilled in the art. The general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather will conform to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. An intelligent signal control method for a light source, characterized in that, It includes the following steps: Step S1: Obtain the environmental light sensing parameters of the material to be cured, conduct analysis on the spatio-temporal distribution of light intensity and fitting of the dynamic light intensity distribution change, and construct a spatio-temporal distribution change map of the curing environment light intensity; Step S2: Collect the light source parameters of the UV lamp bead array; adjust the corresponding lamp bead light source parameters according to the spatio-temporal distribution change map of the curing environment light intensity to obtain a differential UV light source parameter adjustment strategy; Step S3: Conduct real-time curing irradiation on the material to be cured according to the differential UV light source parameter adjustment strategy, and adjust the curing light source intensity and curing time to construct an intelligent curing control strategy; Step S4: Based on the intelligent curing control strategy, conduct real-time curing state mining and rolling prediction of the curing situation in the future period, and generate curing situation prediction maps for multiple time windows; Step S5: Obtain the preset curing log, detect the curing deviation area of the material curing situation prediction map, and conduct deviation compensation calculation to obtain an adaptive curing compensation parameter; Step S6: Conduct iterative collaborative control optimization according to the adaptive curing compensation parameter and the intelligent curing control strategy to construct an intelligent collaborative curing light source control model.

2. The method for controlling a light source intelligent signal machine according to claim 1, wherein, The specific steps of Step S1 are as follows: Obtain the environmental light sensing parameters of the material to be cured; Conduct environmental global light intensity calculation on the curing environment light sensing parameters of the material to be cured, and extract the ambient light intensity around the environment; Identify the environmental light distribution data of the environmental light sensing parameters; Conduct spatio-temporal distribution analysis on the ambient light intensity around the environment and the environmental light distribution data to obtain the spatio-temporal distribution law of the curing environment light intensity; Conduct fitting of the dynamic light intensity distribution change according to the spatio-temporal distribution law of the curing environment light intensity to construct a spatio-temporal distribution change map of the curing environment light intensity.

3. The method for controlling a light source intelligent signal machine according to claim 1, wherein The specific steps of Step S2 are as follows: Collect the light source parameters of the UV lamp bead array based on the light source intelligent signal machine; Conduct identification of the light source parameters of each lamp bead on the light source parameters of the UV lamp bead array, and extract the UV light source intensity and wavelength parameters of each UV lamp bead; Conduct evolution of the light intensity change trend based on the spatio-temporal distribution change map of the curing environment light intensity to obtain the evolution characteristics of the light intensity change trend; Conduct multi-region light intensity difference identification on the evolution characteristics of the light intensity change trend to generate light intensity difference characteristics in different regions; Adjust the corresponding lamp bead light source parameters of the UV light source intensity and wavelength parameters according to the light intensity difference characteristics in different regions to obtain a differential UV light source parameter adjustment strategy.

4. The method for controlling a light source intelligent signal machine according to claim 1, wherein The specific steps of Step S3 are as follows: Conduct real-time curing irradiation on the material to be cured according to the differential UV light source parameter adjustment strategy, and collect real-time curing irradiation spectrum monitoring images; Identify the position of the material to be cured according to the curing irradiation spectrum monitoring images, and conduct multi-spectral response characteristic analysis to extract the multi-spectral response characteristics of the material to be cured; Conduct optical change analysis during the curing process according to the multi-spectral response characteristics of the material to be cured to obtain the optical change information of the material to be cured; Conduct multi-period sensitivity analysis on the optical change information of the material to be cured to obtain the UV light source sensitivity at different irradiation times; Evaluate the curing process matching degree according to the UV light source sensitivity under different irradiation times to generate the curing process matching degree; Adjust the curing light source intensity and curing time according to the curing process matching degree to construct an intelligent curing control strategy.

5. The method for controlling a light source intelligent signal machine according to claim 4, wherein The specific steps of identifying the position of the material to be cured according to the curing irradiation spectrum monitoring image and performing multi-spectral response feature analysis to extract the multi-spectral response features of the material to be cured are as follows: Perform target visual positioning according to the curing irradiation spectrum monitoring image and mark the material to be cured; Calculate the spatial coordinates of the material to be cured; Analyze the spectral changes at different time points according to the spatial coordinates and extract the multi-time point spectral change features of the material to be cured; Calculate the material UV light source reflectivity based on the multi-time point spectral change features to generate the UV light source reflectivity; Analyze the surface color temperature change features of the multi-time point spectral change features; Perform multi-spectral response feature analysis according to the UV light source reflectivity and the surface color temperature change features to extract the multi-spectral response features of the material to be cured.

6. The intelligent signal lamp control method according to claim 1, wherein The specific steps of step S4 are as follows: Perform real-time light source curing process analysis on the real-time curing irradiation spectrum monitoring image based on the intelligent curing control strategy and extract multi-curing stage response data; Analyze the distribution of material temperature changes for the multi-curing stage response data to generate the material temperature fluctuation distribution characteristics; Analyze the trend of the surface texture color of the material according to the multi-curing stage response data and extract the surface texture color trend characteristics; Mine the real-time curing state based on the material temperature fluctuation distribution characteristics and the surface texture color trend characteristics to obtain the real-time curing state; Define the time window length; Perform rolling prediction of the curing trend in the future period on the real-time curing state based on the time window length to generate the curing trend prediction diagrams of the material for multiple time windows.

7. The intelligent signal machine control method for a light source according to claim 1, wherein The specific steps of step S5 are as follows: Obtain the preset curing log and perform final identification of the material curing effect to generate the final material curing effect; Calculate the curing uniformity for the curing trend prediction diagrams of the material for multiple time windows to generate the predicted value of the material curing uniformity; Detect the curing deviation area for the predicted value of the material curing uniformity according to the final material curing effect and mark the curing deviation area; Perform in-depth analysis of the deviation trend for the curing deviation area to generate the area deviation trend characteristics; Perform adaptive curing deviation compensation calculation on the area deviation trend characteristics to obtain the adaptive curing compensation parameters.

8. The method for controlling a light source intelligent signal machine according to claim 1, characterized in that The specific steps of step S6 are as follows: Perform fine-tuning of the UV light source power output compensation on the light source intelligent signal machine according to the adaptive curing compensation parameters to obtain the output compensation light source power; Optimize the real-time output power of the output compensation light source power to generate the optimized light source output power; Perform iterative collaborative control optimization based on the optimized light source output power and the intelligent curing control strategy to construct an intelligent collaborative curing light source control model; Upload the intelligent collaborative curing light source control model to the light source intelligent signal machine to execute the UV curing control operation.

9. An intelligent signal machine control system for a light source, characterized in that, For executing the light source intelligent signal machine control method as described in claim 1, including: The light intensity spatio-temporal distribution module is used to obtain the environmental light sensing parameters of the material to be cured, perform light intensity spatio-temporal distribution analysis and fitting of dynamic light intensity distribution changes, and construct a spatio-temporal distribution change diagram of the curing environment light intensity; The differential adjustment module is used to collect the light source parameters of the UV lamp bead array; adjust the corresponding lamp bead light source parameters according to the spatio-temporal distribution change diagram of the curing environment light intensity to obtain a differential UV light source parameter adjustment strategy; The curing parameter adjustment module is used to perform real-time curing irradiation on the material to be cured according to the differential UV light source parameter adjustment strategy, and adjust the curing light source intensity and curing time to construct an intelligent curing control strategy; The curing situation prediction module is used to perform real-time curing state mining and rolling prediction of the curing situation in the future period based on the intelligent curing control strategy, and generate curing situation prediction diagrams of multiple time windows; The adaptive compensation module is used to obtain the preset curing log, detect the curing deviation area of the material curing situation prediction diagram, and perform deviation compensation calculation to obtain the adaptive curing compensation parameter; The collaborative control optimization module is used to perform iterative collaborative control optimization according to the adaptive curing compensation parameter and the intelligent curing control strategy, and construct an intelligent collaborative curing light source control model.

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