Automatic light source control method and system

Abstract

The invention relates to the technical field of light attenuation compensation control, in particular to an automatic light source control method and system. The method comprises the following steps: acquiring historical light source operation state parameters, performing long-time-sequence operation state change evolution, and constructing a long-time-sequence light source state change curve; performing nonlinear light source attenuation analysis and multi-period light source attenuation evolution on the long-time sequence light source state change curve to construct a light source attenuation evolution model; acquiring real-time light source operation state monitoring parameters and environment illumination signals; inputting into a light source attenuation evolution model, and carrying out light source attenuation evolution simulation to obtain a light source attenuation gradient situation; and acquiring real-time working current and voltage parameters of the automatic light source, and performing adaptive light attenuation compensation optimization based on the attenuation gradient situation of the light source so as to generate an adaptive light attenuation compensation optimization engine. According to the invention, the accuracy and effect of overall compensation control of light attenuation of the light source are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of light decay compensation control, and particularly to an automatic light source control method and system. Background Art

[0002] In the wide application of modern industrial automation and intelligent lighting systems, as a key lighting device, the light source undertakes the important task of providing stable and uniform illumination. With the complexity of the production environment and application scenarios, performance parameters such as the service life, brightness, and color temperature of the light source will decay over time. Especially in the case of long-term continuous operation, the decay phenomenon of the light source becomes more obvious, directly affecting the lighting quality, and may even lead to non-compliance with the predetermined lighting requirements, thereby affecting production efficiency and user experience.

[0003] Traditional light source decay detection methods usually rely on manual periodic detection and manual adjustment. The decay process of the light source is often difficult to detect in a timely manner and precisely controlled. Since manual detection relies on subjective experience and has a slow processing speed, it is often difficult to adjust the working state of the light source in a timely manner, resulting in waste of light source efficiency and increased energy consumption. In addition, most of the existing control methods lack the dynamic adaptation ability to light source decay and cannot adjust the output of the light source in real time according to the actual working environment and usage status, resulting in fluctuations and instability of the light source performance. In the context of intelligent lighting and industrial automation, with the rapid development of intelligent sensor and data processing technologies, the automatic monitoring and intelligent compensation of light source light decay have become an important research direction for modern lighting systems. Summary of the Invention

[0004] To solve the above technical problems, the present invention proposes an automatic light source control method and system to solve at least one of the above technical problems.

[0005] To achieve the above object, the present invention provides an automatic light source control method, including the following steps: Step S1: Obtain historical light source operation state parameters, and perform long-time series operation state change evolution to construct a long-time series light source state change curve; Step S2: Perform non-linear light source decay analysis and multi-period light source decay evolution on the long-time series light source state change curve to construct a light source decay evolution model; Step S3: Obtain real-time light source operation state monitoring parameters and ambient light signals; and input them into the light source decay evolution model to perform light source decay evolution simulation, so as to obtain the light source decay gradient trend; Step S4: Obtain the real-time working current and voltage parameters of the automatic light source, and perform adaptive light decay compensation optimization based on the light source decay gradient trend, so as to generate an adaptive light decay compensation optimization engine; Step S5: Mine the temporal variation of the ambient light in the ambient light signal, perform optical feature situation modeling, and construct an ambient light change situation map; Step S6: Dynamically fine-tune the adaptive optical decay compensation optimization engine according to the ambient light change situation map to construct a dynamic optical decay compensation control model.

[0006] By obtaining the historical operating state parameters of the light source, the present invention can comprehensively understand the performance and attenuation trend of the light source at different usage stages. Long-time series data not only helps to capture the normal operating state of the light source, but also can identify special changes (such as attenuation patterns and abnormal fluctuations) during long-term operation. By analyzing the changes in the long-time series of the light source in detail and constructing a light source state change curve, various laws during the attenuation process of the light source can be accurately depicted. This curve provides a valuable data basis for subsequent compensation and adjustment, and helps to better understand the decline trend and life cycle characteristics of the light source. The attenuation process of the light source is usually non-linear, especially for light sources used for a long time. By performing non-linear attenuation analysis on historical data, the complexity of attenuation can be revealed, such as the characteristics of slower initial attenuation and accelerated later attenuation, so as to more accurately predict the future attenuation of the light source. The attenuation of the light source is not just a single linear process, and it will have different evolution trends over time and with changes in the working environment. By analyzing the attenuation laws in different time periods, a more detailed attenuation model can be established, improving the accuracy and flexibility of the compensation algorithm. The multi-period light source attenuation evolution model can provide different compensation strategies at different usage stages (such as the initial stage, the middle stage, and the later stage), making the compensation process more accurate and efficient. This compensation mechanism based on the multi-period attenuation model can adapt to changes in different scenarios and time periods, improving the overall performance of the system. By obtaining the working state parameters (such as current, voltage, brightness, color temperature, etc.) of the light source and the ambient light signal in real time, the current light source state can be compared with historical data to perform real-time attenuation evolution simulation. This makes the prediction and compensation of the attenuation process more in line with the current usage situation. By simulating the attenuation evolution of the light source, the gradient trend of the light source attenuation can be obtained, that is, the attenuation changes at different time points and under different environmental conditions. This helps to refine the intensity and timing of the compensation, making the compensation more flexible and targeted, and avoiding the situations of "over-compensation" or "under-compensation". Combining the real-time light source state and ambient light data, the compensation strategy can be dynamically adjusted to ensure that the light source light attenuation compensation can achieve the best effect at different time points and in different scenarios. This real-time simulation improves the response speed and accuracy of the entire system. Real-time monitoring of the working current and voltage of the automated light source helps to track the electrical performance of the light source and timely detect the decline or efficiency reduction of the light source caused by changes in current and voltage. This is the basis for ensuring the working stability of the light source and avoiding damage. According to the light source attenuation gradient trend and real-time working current and voltage data, compensation optimization is carried out through an adaptive algorithm. This method can intelligently adjust the amplitude, timing, and method of compensation according to the actual attenuation situation, enabling the light source to perform real-time compensation according to the current attenuation state and avoiding manual intervention. The generated adaptive light attenuation compensation optimization engine can dynamically adjust the working parameters (such as current, frequency, temperature, etc.) of the light source, ensuring the efficiency and accuracy of the compensation process, while increasing the service life of the light source and the energy efficiency of the system.By performing a timing analysis of the ambient light signal, the changing patterns of ambient light at different time periods can be discovered, thereby understanding the impact on the light source output under different ambient light conditions. This process helps the system understand the lighting patterns of the external environment, thus optimizing the light source output. Constructing a graph of the changing situation of ambient light can clearly show the changing trends of ambient light at different time periods and conditions, helping the system comprehensively understand the potential impact of environmental changes on the light source. In this way, the system can make timely adjustments based on environmental changes, avoiding affecting the user experience due to overly strong or weak light. Modeling the changing situation of ambient light helps to consider the influence of external light sources during the compensation process, enabling the system to adjust the compensation intensity according to the actual changes in the environment, improving the accuracy and effectiveness of overall compensation. By dynamically adjusting the adaptive light attenuation compensation optimization engine according to the graph of the changing situation of ambient light, the compensation strategy of the light source can respond to changes in ambient light in real time, thereby providing more accurate light compensation. This mechanism effectively avoids the adverse effects of ambient light changes on the light source output. By constructing a dynamic light attenuation compensation control model, the compensation strategy can be fine-tuned during the operation of the light source to adapt to different working environments and light source states. This not only improves the compensation accuracy but also ensures that the energy efficiency and performance of the light source are maximally exerted. The construction of the dynamic light attenuation compensation control model can optimize the light source output in real time, enabling users to always obtain a comfortable lighting effect in different lighting environments and avoiding experience fluctuations caused by light source attenuation or changes in ambient light.

[0007] In this specification, a control system is provided for implementing the automated light source control method described above, including: A light source status module for obtaining historical light source operation status parameters and performing long-term timing operation status change evolution to construct a long-term timing light source status change curve; A non-linear light attenuation analysis module for performing non-linear light source attenuation analysis and multi-period light source attenuation evolution on the long-term timing light source status change curve to construct a light source attenuation evolution model; A light attenuation evolution simulation module for obtaining real-time light source operation status monitoring parameters and ambient light signals; and inputting them into the light source attenuation evolution model to perform light source attenuation evolution simulation, thereby obtaining a light source attenuation gradient situation; An adaptive light attenuation compensation module for obtaining the real-time working current and voltage parameters of the automated light source and performing adaptive light attenuation compensation optimization based on the light source attenuation gradient situation, thereby generating an adaptive light attenuation compensation optimization engine; An ambient light change module for mining the ambient light timing changes of the ambient light signal and performing light feature situation modeling to construct a graph of the changing situation of ambient light; A light attenuation compensation control module for dynamically fine-tuning the adaptive light attenuation compensation optimization engine according to the graph of the changing situation of ambient light to construct a dynamic light attenuation compensation control model.

[0008] Through the accumulation of long-time series data, the present invention can accurately track the whole process of the light source from the start of use to attenuation, reflecting the state change trend of the light source at different stages. The light source state module can provide a scientific basis for subsequent attenuation analysis, timely identify potential decline risks, ensure the early detection of abnormal states, and reduce the performance degradation or failure of the light source caused by excessive attenuation. It provides accurate historical data support for subsequent light attenuation evolution modeling and compensation algorithms, enhancing the accuracy and reliability of the system. Through non-linear analysis, it can handle the complex attenuation characteristics of actual light sources, which may not only change linearly over time but also be non-linearly affected by factors such as environment and usage frequency, ensuring more accurate attenuation prediction. By analyzing the attenuation data in multiple time periods, it can identify the decline characteristics of the light source under different usage conditions, further improving the flexibility and adaptability of the prediction. Based on the output of this model, personalized attenuation compensation schemes can be set for different light sources, achieving efficient attenuation prediction and adjustment. By real-time input of the light source operating state and ambient light data, the decline process of the light source can be dynamically simulated. Compared with static analysis, this real-time simulation can better cope with the impacts brought by the changes in the light source state and environment. Through the simulated light source attenuation gradient trend, the rate and amplitude of attenuation can be determined, providing high-precision input for subsequent compensation. It provides real-time feedback for the adaptive light attenuation compensation module, helping the system better understand the current state of the light source, and thus making more accurate adjustments. Through the adaptive compensation strategy, parameters such as current, voltage, and temperature can be adjusted according to real-time data to optimize the operating state of the light source and offset the brightness and performance degradation caused by attenuation. According to different light source types, operating conditions, and environmental changes, the system can flexibly adjust the compensation strategy to ensure the long-term stable performance of the light source. Adaptive compensation can avoid over-compensation, save energy, extend the service life of the light source, and at the same time maintain the stability and efficiency of the light source output. It can identify and capture environmental light changes (such as the changes between day and night, different seasons or weather conditions, etc.), ensuring that the light source can adjust its output according to environmental conditions and avoiding the performance deviation of the light source under the influence of environmental light. Through in-depth analysis of the ambient light characteristics, an accurate ambient light change trend map can be constructed, providing accurate data support for the environmental adaptability adjustment of the light source. By dynamically adjusting the light source output and compensating according to real-time ambient light changes, it ensures that the light source always maintains the optimal lighting effect, while avoiding over-compensation or under-compensation. Dynamically fine-tuning the compensation intensity enables the system to make subtle and accurate adjustments according to the changes in the environment and the light source state, ensuring the stability of parameters such as the brightness and color temperature of the light source. By constructing a dynamic light attenuation compensation control model, it can not only adjust the light source in real time but also automatically adjust the light source compensation intensity according to environmental changes, achieving global light source optimization. Description of the Drawings

[0009] Figure 1Schematic diagram of the step flow of an automated light source control method of the present invention; Figure 2 Schematic diagram of the detailed implementation steps of step S1; Figure 3 Schematic diagram of the detailed implementation steps of step S2; Figure 4 Schematic diagram of the detailed implementation steps of step S3. Detailed implementation manner

[0010] 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.

[0011] The embodiments of the present application provide an automated light source control method and system. The execution subjects of the automated light source control method and system include, but are not limited to, the following that carry the system: mechanical equipment, data processing platforms, cloud server nodes, network upload devices, etc., 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.

[0012] Please refer to Figures 1 to 4 , the present invention provides an automated light source control method, and the automated light source control method includes the following steps: Step S1: Obtain historical light source operating state parameters, perform long-time series operating state change evolution, and construct a long-time series light source state change curve; Step S2: Perform non-linear light source attenuation analysis and multi-period light source attenuation evolution on the long-time series light source state change curve to construct a light source attenuation evolution model; Step S3: Obtain real-time light source operating state monitoring parameters and ambient light signals; and input them into the light source attenuation evolution model to perform light source attenuation evolution simulation, so as to obtain the light source attenuation gradient trend; Step S4: Obtain the real-time working current and voltage parameters of the automated light source, and perform adaptive light attenuation compensation optimization based on the light source attenuation gradient trend, so as to generate an adaptive light attenuation compensation optimization engine; Step S5: Mine the ambient light time series change of the ambient light signal, and perform light feature trend modeling to construct an ambient light change trend map; Step S6: Perform dynamic light attenuation compensation fine-tuning on the adaptive light attenuation compensation optimization engine according to the ambient light change trend map to construct a dynamic light attenuation compensation control model.

[0013] By obtaining the historical operating state parameters of the light source, the present invention can comprehensively understand the performance and attenuation trend of the light source at different usage stages. Long-time series data not only helps to capture the normal operating state of the light source, but also can identify special changes (such as attenuation patterns and abnormal fluctuations) during long-term operation. By analyzing the changes in the long-time series of the light source in detail and constructing a light source state change curve, various laws during the attenuation process of the light source can be accurately depicted. This curve provides a valuable data basis for subsequent compensation and adjustment, and helps to better understand the decline trend and life cycle characteristics of the light source. The attenuation process of the light source is usually non-linear, especially for light sources used for a long time. By performing non-linear attenuation analysis on historical data, the complexity of attenuation can be revealed, such as the characteristics that the initial attenuation is slow and the later attenuation intensifies, so as to more accurately predict the future attenuation of the light source. The attenuation of the light source is not just a single linear process, and it will have different evolution trends over time and with changes in the working environment. By analyzing the attenuation laws in different time periods, a more detailed attenuation model can be established, improving the accuracy and flexibility of the compensation algorithm. The multi-period light source attenuation evolution model can provide different compensation strategies at different usage stages (such as the initial stage, the middle stage, and the later stage), making the compensation process more accurate and efficient. This compensation mechanism based on the multi-period attenuation model can adapt to changes in different scenarios and time periods, improving the overall performance of the system. By obtaining the working state parameters (such as current, voltage, brightness, color temperature, etc.) of the light source and the ambient light signal in real time, the current light source state can be compared with the historical data to perform real-time attenuation evolution simulation. This makes the prediction and compensation of the attenuation process more in line with the current usage situation. By simulating the attenuation evolution of the light source, the gradient trend of the light source attenuation can be obtained, that is, the attenuation changes at different time points and under different environmental conditions. This helps to refine the intensity and timing of the compensation, making the compensation more flexible and targeted, and avoiding the situations of "over-compensation" or "under-compensation". Combining the real-time light source state and ambient light data, the compensation strategy can be dynamically adjusted to ensure that the light source light attenuation compensation can achieve the best effect at different time points and in different scenarios. This real-time simulation improves the response speed and accuracy of the entire system. Real-time monitoring of the working current and voltage of the automated light source helps to track the electrical performance of the light source and timely detect the decline or efficiency reduction of the light source caused by changes in current and voltage. This is the basis for ensuring the working stability of the light source and avoiding damage. According to the light source attenuation gradient trend and the real-time working current and voltage data, compensation optimization is performed through an adaptive algorithm. This method can intelligently adjust the amplitude, timing, and method of compensation according to the actual attenuation situation, enabling the light source to perform real-time compensation according to the current attenuation state and avoiding manual intervention. The generated adaptive light attenuation compensation optimization engine can dynamically adjust the working parameters (such as current, frequency, temperature, etc.) of the light source, ensuring the efficiency and accuracy of the compensation process, while improving the service life of the light source and the energy efficiency of the system.By performing a timing analysis of the ambient light signal, the changing patterns of the ambient light at different time periods can be discovered, thereby understanding the impact on the light source output under different ambient light conditions. This process helps the system understand the lighting patterns of the external environment, thus optimizing the light source output. Constructing a graph of the changing trend of the ambient light can clearly display the changing trends of the ambient light at different time periods and conditions, helping the system comprehensively understand the potential impact of environmental changes on the light source. In this way, the system can make timely adjustments based on the changes in the environment, avoiding affecting the user experience due to overly strong or weak light. Modeling the changing trend of the ambient light helps to consider the influence of external light sources during the compensation process, enabling the system to adjust the compensation intensity according to the actual changes in the environment, improving the accuracy and effectiveness of the overall compensation. By dynamically adjusting the adaptive light decay compensation optimization engine according to the graph of the changing trend of the ambient light, the compensation strategy of the light source can respond to the changes in the ambient light in real time, thus providing more accurate light compensation. This mechanism effectively avoids the adverse effects of ambient light changes on the light source output. By constructing a dynamic light decay compensation control model, the compensation strategy can be fine-tuned during the operation of the light source to adapt to different working environments and light source states. This not only improves the compensation accuracy but also ensures that the energy efficiency and performance of the light source are maximized. The construction of the dynamic light decay compensation control model can optimize the light source output in real time, enabling users to always obtain a comfortable lighting effect in different lighting environments and avoiding experience fluctuations caused by light source decay or ambient light changes.

[0014] In an embodiment of the present invention, refer to Figure 1 , which is a schematic diagram of the step flow of an automated light source control method of the present invention. In this example, the steps of the automated light source control method include: Step S1: Obtain the historical operating state parameters of the light source, and perform long-time series evolution of the operating state changes to construct a long-time series light source state change curve; In this embodiment, the data source for determining the historical operating state parameters of the light source can be the light source control system, data recorder, or sensor network. These devices should have the ability to record the operating state of the light source, including parameters such as luminous flux, color temperature, color rendering index, current, and voltage. Ensure the reliability of the data source by selecting verified devices and systems to reduce errors in the data acquisition process. Start the data acquisition system to extract the historical operating state parameters of the light source. The collected data should cover a certain time range (such as the past 6 months or 1 year) to ensure the representativeness of the analysis. Record the timestamp for each data point to ensure the chronological order of the data. For example, record parameters such as luminous flux (lumens), voltage (volts), and current (amperes) to ensure that the data at each time point can be corresponded to each other. Organize the collected historical data into a structured format, such as a CSV or SQL database, to ensure the integrity and readability of the data. Each record should include the timestamp, light source parameters, and their units, such as "2023-01-01 12:00:00, 800 lumens, 220 volts, 0.5 amperes". Conduct preliminary data cleaning to remove duplicate values and outliers to improve the quality of the data and the accuracy of the analysis. Preprocess the collected historical operating state parameters of the light source to ensure the stability and consistency of the data. Interpolation methods can be used to fill in missing data to ensure the continuity of the time series. Perform data normalization to convert the values of different parameters to the same dimension for subsequent comparison and analysis. For example, the luminous flux can be normalized to the range of 0 to 1. Select appropriate time series analysis methods, such as moving average method, exponential smoothing method, or ARIMA model, to analyze the long-term time series change characteristics of the light source. These methods can effectively capture the change trends of the light source state. Set the analysis time window, such as daily, weekly, or monthly, and observe the change of the light source operating state through different time scales. Based on the processed data, construct an evolution model of the light source state change. A linear regression model can be used to fit the trend of the light source parameters changing with time, and record the parameters and goodness of fit of the model to evaluate the effectiveness of the model. Generate change curves to show the changes of parameters such as luminous flux, color temperature, and current over time, and record the change characteristics of each time period, such as rising, falling, or stable. Select suitable data visualization tools, such as Excel, Tableau, or Python visualization libraries (such as Matplotlib), to generate intuitive long-term time series change curves. Visualization can help identify the patterns and trends of the light source state change. Ensure that the tool can handle large-scale data and support the generation of multiple chart types, such as line charts and scatter plots. Use the selected visualization tool to plot the organized long-term time series data. Use time as the abscissa and the operating state parameters of the light source (such as luminous flux, color temperature, and current) as the ordinate to plot the light source state change curve. During the plotting process, use different colors and styles to distinguish different light source parameters to ensure the clarity and readability of the chart.Analyze the generated long-time series change curve, and identify the key features of the light source operation state change, such as significant fluctuations, trend changes, and abnormal points.

[0015] Step S2: Conduct non-linear light source attenuation analysis and multi-period light source attenuation evolution on the long-time series light source state change curve to construct a light source attenuation evolution model; In this embodiment, before conducting non-linear light source attenuation analysis, ensure that the data of the long-time series light source state change curve has been sorted out. The data should include parameters such as timestamp, luminous flux, color temperature, and current, ensuring that the time series of each data point is complete and consistent. Smooth the luminous flux data to reduce the influence of noise. Methods such as moving average method or low-pass filter can be used for smoothing to ensure the accuracy of the analysis. For example, use the 7-day moving average method to calculate the smoothed value of the luminous flux. Select a suitable non-linear regression model to analyze the light source attenuation, such as polynomial regression model, exponential decay model, or Logistic model. These models can effectively capture the non-linear characteristics of the light source attenuation. Determine the key parameters of the model, such as attenuation rate, time constant, and initial luminous flux, and these parameters will be estimated in subsequent modeling. Divide the data of the long-time series light source state change curve into multiple time periods for multi-period light source attenuation evolution analysis. It can be divided according to time (such as day, week, month) or the change characteristics of the luminous flux. Each time period should contain sufficient data points to ensure the reliability of the analysis. For example, select the luminous flux data of each week for analysis, and each segment of data should contain at least 7 days of records. For the data of each time period, apply the selected non-linear model for fitting, and extract the characteristic parameters of the light source attenuation. Record the fitting parameters of each time period, including attenuation rate and time constant, to analyze the attenuation characteristics of the light source in different time periods. Based on the extracted characteristic parameters of the light source attenuation, construct a complete light source attenuation evolution model. Use non-linear regression analysis method to estimate the parameters of the model to ensure that the model can accurately fit the historical data. Conduct model verification, and evaluate the accuracy and reliability of the model by calculating the goodness of fit (such as R² value) and residual analysis. Ensure that the model can effectively capture the dynamic changes of the light source attenuation. Use a visualization tool to compare and display the established light source attenuation evolution model with the actual luminous flux data, and generate a light source attenuation curve graph. This can intuitively show the fitting effect of the model. Mark the attenuation characteristics of different time periods in the graph to help identify the trends and patterns of the light source attenuation. For example, different colors can be used to represent the attenuation curves of different time periods and explained in the legend.

[0016] Step S3: Obtain the real-time light source operation state monitoring parameters and environmental light signal; and input them into the light source attenuation evolution model for light source attenuation evolution simulation, so as to obtain the light source attenuation gradient trend; In this embodiment, ensure that the real-time monitoring devices of the light source (such as photoelectric sensors, current sensors, and voltage sensors) are correctly installed and configured. The sensors should have a high sampling frequency and accuracy to capture the operating state of the light source in real time. Set up the monitoring system to ensure that it can record various parameters of the light source, including luminous flux (lumens), current (amperes), voltage (volts), and temperature (degrees Celsius), etc. These parameters are crucial in the performance evaluation of the light source. Start the monitoring system to begin real-time collection of the operating state parameters of the light source. It is recommended to collect data in units of seconds to facilitate capturing rapid changes in the light source state. Record the light source parameters at each time point and their corresponding timestamps to ensure the chronological order of the data. For example, the recorded data format is "2023-10-01 12:00:00, 850 lumens, 0.6 amperes, 220 volts". Install ambient light sensors to ensure that they can effectively monitor the ambient light intensity. These sensors should have real-time monitoring capabilities and be able to record changes in ambient light. Configure the sampling frequency of the sensors to be consistent with the monitoring system of the light source to ensure data synchronization. It is recommended to set it to sample once per second to capture rapidly changing ambient light conditions. Start the ambient light sensors to begin real-time collection of ambient light signals. Record the light intensity value (in lux) at each time point and attach the timestamp. For example, the recorded data format is "2023-10-01 12:00:00, 300". Ensure the accuracy and consistency of the data for subsequent analysis. Store the ambient light signals in the same database as the light source monitoring data to ensure data structuring and consistency. Associate the ambient light signals with the light source parameters through timestamps to provide a basis for subsequent analysis. Perform preliminary cleaning to remove outliers and missing values to ensure the usability of the ambient light data. After the real-time monitoring data collection is completed, input the operating state parameters of the light source and the ambient light signals into a pre-established light source attenuation evolution model. The model should have been verified to accurately simulate the attenuation characteristics of the light source. Ensure that the input data format is consistent with the model requirements. For example, the input luminous flux, ambient light intensity, current, and voltage should all be numerical and corresponding to timestamps. According to the input data, start the light source attenuation evolution model for simulation. The model will use real-time parameters to calculate the current attenuation of the light source and obtain the light source attenuation gradient. During the simulation process, the model will consider the influence of ambient light and combine the operating state of the light source to calculate the attenuation degree of the light source under the current environmental conditions. For example, the model may calculate how much the luminous flux of the light source has decreased under specific ambient light. Record the simulated light source attenuation gradient trend, including the calculated luminous flux, attenuation rate, and current state. These results will provide an important basis for subsequent light source management and optimization.

[0017] Step S4: Obtain the real-time working current and voltage parameters of the automated light source, and perform adaptive optical attenuation compensation optimization based on the optical source attenuation gradient trend, thereby generating an adaptive optical attenuation compensation optimization engine; In this embodiment, ensure that the current and voltage sensors are correctly installed on the power line of the automated light source. These sensors should have real-time monitoring capabilities and be able to accurately record the operating current and voltage parameters of the light source. Configure the sampling frequency of the sensors, and it is recommended to set it to sample once per second to facilitate real-time monitoring of the current and voltage changes of the light source and ensure the timeliness of the data. Start the monitoring system to begin real-time collection of the current and voltage parameters of the light source. Each data point should include a timestamp, the current current value (in amperes), and the voltage value (in volts) to facilitate subsequent analysis. For example, the recorded data format is "2023-10-01 12:00:00, 0.5 amperes, 220 volts". Ensure the accuracy and consistency of the data to facilitate correlation with other operating state parameters of the light source. Store the real-time collected current and voltage data in a database using a structured format (such as CSV or SQL) to ensure the integrity and readability of the data. During the data storage process, establish a corresponding structure for subsequent analysis. Perform preliminary data cleaning to remove obvious outliers and missing values. If the current value exceeds the set maximum safety value, mark it as abnormal and record it for subsequent analysis. Design an adaptive compensation strategy based on the light source attenuation gradient trend obtained in the previous steps. This strategy should automatically adjust the output of the light source to compensate for light attenuation based on real-time current and voltage parameters and the attenuation of the light source. Determine the compensation rules. For example, when the luminous flux of the light source is lower than the preset threshold, the system should automatically increase the current output to maintain the illumination intensity. Combine the real-time collected current and voltage parameters with the light source attenuation gradient trend and input them into the compensation optimization engine for analysis. The engine should calculate the current state of the light source in real time and determine whether light attenuation compensation is required. For example, if the current luminous flux is 700 lumens and the optimal luminous flux is 850 lumens, the compensation engine will calculate the required increase in current value to reach the required luminous flux. Automatically adjust the current output of the light source according to the calculation result of the compensation engine. For example, if the calculation result shows that an increase of 0.1 amperes in current is required, the system will automatically adjust the current controller to achieve compensation. Record the process and effect of each compensation adjustment to ensure the effectiveness and accuracy of each adjustment. If the compensation is successful, the system will update the current luminous flux value and continue monitoring. Design the architecture of the adaptive light attenuation compensation optimization engine, including a data input module, a decision-making module, and an output control module. The input module is responsible for receiving real-time current and voltage and light source attenuation data, the decision-making module is responsible for analysis and calculation, and the output module is responsible for controlling the current output of the light source. Ensure that the engine can respond to environmental changes in real time and make dynamic adjustments according to the attenuation situation to achieve the best lighting effect. Test the adaptive light attenuation compensation optimization engine in actual applications to verify its effectiveness under different environmental lighting conditions and light source operating states. Collect feedback data to evaluate the performance of the engine, including its response time, compensation accuracy, and stability. Make necessary adjustments and optimizations according to the test results to ensure that the engine can adapt to different working environments and light source characteristics.Record the operating status and compensation effect of the recording engine, including the parameters of each compensation, the change in the luminous flux of the light source, and the influence of ambient light. Generate a report that details the process, results of compensation optimization, and its impact on the performance of the light source. Based on the analysis results, provide suggestions for subsequent light source management and optimization, such as how to adjust the compensation mechanism according to different ambient light conditions to improve the usage efficiency and lifespan of the light source.

[0018] Step S5: Mine the temporal variation of the ambient light for the ambient light signal, perform optical feature situation modeling, and construct an ambient light change situation map; In this embodiment, it is ensured that the real-time data of the ambient light signal has been effectively collected. The data should include information such as timestamps and light intensity (in lux) to ensure the integrity and accuracy of the data. Organize the collected ambient light data into a structured format to form a time series data table, ensuring that each data point can clearly reflect the changes in ambient light. For example, the recording format is "2023-10-01 12:00:00, 300 lux". Select suitable time series analysis methods, such as autocorrelation analysis, seasonal decomposition, moving average method, etc., to mine the time series change characteristics of ambient light. The analysis method should be able to effectively capture the trends, cycles, and sudden changes in the data. Set the analysis time window, for example, analyze by hour, day, or week to observe the ambient light change characteristics at different time scales. Apply the selected time series analysis method to identify the change patterns in the ambient light signal. For example, identify the periodic changes in light intensity through autocorrelation analysis, or extract the trend component and seasonal component through seasonal decomposition. Record the extracted features, including the maximum value, minimum value, mean value, and change rate of light intensity, etc. These features will provide the basic data for subsequent situation modeling. According to the extracted ambient light characteristics, select suitable modeling methods, such as state space models, time series prediction models (such as ARIMA), or machine learning models (such as random forest or neural network). These models should be able to effectively capture the dynamic change characteristics of the light signal. Determine the key parameters of the model, such as seasonal factors, trend terms, and noise terms, to ensure that the model can accurately fit the historical data. Use the organized ambient light characteristic data to train the selected model. During the training process, divide the data into a training set and a test set to facilitate the evaluation of the model's performance. Conduct model validation, and evaluate the accuracy and reliability of the model by calculating the goodness of fit (such as R² value) and prediction error (such as root mean square error RMSE). Ensure that the model can effectively capture the change trend of ambient light. Design the framework of the ambient light change situation map, and determine the type and display content of the chart. Forms such as line charts, heat maps, or bar charts can be used to display the change trend and important features of ambient light intensity. Ensure that the chart can clearly display the time series data, including the change in light intensity, trend line, predicted value, and its confidence interval. Use data visualization tools (such as Tableau, Excel, or Python visualization libraries) to plot the organized ambient light data and model prediction results into a situation map. Ensure that the design of the chart is beautiful, clear, and easy to understand. Use different colors and markers in the chart to distinguish the actual data from the predicted data, and explain it in the legend to facilitate users to quickly identify important information.

[0019] Step S6: Dynamically fine-tune the dynamic light attenuation compensation of the adaptive light attenuation compensation optimization engine according to the ambient light change situation map, and construct a dynamic light attenuation compensation control model.

[0020] In this embodiment, analyze the changing trend of the light intensity in the analysis chart and identify obvious patterns. For example, if the light intensity significantly decreases during certain periods, it may be necessary to increase the output of the light source during these periods for compensation. Record these changing trends, including the maximum value, minimum value, and average value of the light intensity, to ensure the accuracy and integrity of the data. These metrics will be used for subsequent dynamic compensation fine-tuning. Consider the impact of environmental factors on light changes, such as weather changes, seasonal factors, and the occlusion of surrounding buildings, etc. These factors may cause sudden changes in light intensity and affect the normal operation of the light source. Combine historical data to analyze the relationship between these influencing factors and light changes, so as to make corresponding adjustments in the compensation strategy. Design the framework of the dynamic light attenuation compensation control model, and clarify the input, output, and control logic of the model. The input should include the real-time light intensity, the current state of the light source (such as current, voltage), and the key parameters of the ambient light change trend map. Determine the output of the model as the current adjustment value of the light source. The model should dynamically adjust the output of the light source according to the input data to achieve the required light intensity. Select a suitable control algorithm, such as PID control (Proportional-Integral-Derivative control) or fuzzy control. These algorithms can continuously adjust the light source output according to real-time feedback to achieve the desired lighting effect. Determine the parameter settings of the control algorithm, including the proportional coefficient, integral time, and derivative time, to ensure the stability and response speed of the model. Use historical ambient light data and light source operating state data to train the control model. Through the regression analysis of historical data, optimize the control parameters to ensure that the model can effectively predict and compensate for the attenuation of the light source. Conduct model verification and use test data to evaluate the performance of the model. Compare the current adjustment value output by the model with the change in the actual light intensity to ensure that the model can respond to environmental changes in real time. Input the latest ambient light signal and light source state parameters in real time into the dynamic control model. The system should have a real-time feedback mechanism that can quickly adjust the output of the light source according to the input data. For example, when the ambient light intensity suddenly drops to a certain threshold, the control model should quickly calculate the required current value to compensate for the attenuation of the light source. Adjust the current output of the light source according to the output of the control model. For example, if the control model calculates that it is necessary to increase the current by 0.15 amperes, the system should immediately implement this adjustment. Record the process and its effect of each adjustment to ensure the effectiveness and accuracy of each compensation. Monitor the change in the luminous flux of the light source to ensure that it is stable within the preset range. Regularly evaluate the performance of the dynamic light attenuation compensation control model, including response time, compensation accuracy, and stability. Evaluate the effectiveness of the model by comparing the actual light intensity with the target light intensity. According to the evaluation results, make necessary model adjustments and optimizations. For example, optimize the control parameters or improve the control algorithm to improve the overall performance and response ability of the system.

[0021] 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: Obtain historical light source operation status parameters; Calculate the light output flux of the light source based on the historical light source operation status parameters, and extract the historical light flux parameters of the light source; Analyze the color temperature and color rendering index of the light source based on the historical light source operation status parameters; Mine the light source state characteristics according to the historical light flux parameters of the light source and the color temperature and color rendering index of the light source, so as to generate the light source state characteristics; Perform long-time series operation state change evolution on the light source state characteristics, and construct a long-time series light source state change curve.

[0022] In this embodiment, historical operating status data of the light source is obtained, which includes parameters such as the working voltage, current, power, working time, temperature, etc. of the light source. Data collection can be carried out through the light source control system or monitoring devices (such as data loggers) to ensure the accuracy and integrity of the data. Set the time interval for data collection (such as once per minute), and collect at least one month of operating data for subsequent analysis. The data should be stored in a database in a structured manner to ensure the convenience of subsequent querying and processing. Clean the collected historical data to remove outliers and missing values. For example, if the current data for a certain period is abnormally high or low, it should be marked and processed. Use interpolation or mean methods to fill in the missing values to ensure the continuity and usability of the data. Standardize the preprocessed data to ensure comparison and analysis of different parameters under the same dimension. The Z-score standardization method can be used to convert the data into a standard normal distribution. Organize the cleaned historical light source operating status parameters into a data table to ensure the structuring of information. Record the light source status parameters at each time point for subsequent luminous flux calculation and characteristic analysis. Generate a data analysis report, recording the process and results of data collection and preprocessing, providing a basis for subsequent research. Determine the calculation formula for the luminous flux output by the light source. For most light sources, the following formula can be used for calculation: Φ = P ⋅ η where Φ represents the luminous flux (in lumens), P represents the power of the light source (in watts), and η represents the luminous efficacy (lumens / watt). Use the power data in the historical light source operating status parameters, combined with the luminous efficacy data, to calculate the luminous flux at each time point. Record the parameters and formulas used in the calculation process to ensure the transparency and traceability of the calculation. For different types of light sources, the luminous efficacy parameters may need to be adjusted. For example, for LEDs and fluorescent lamps, the change in luminous efficacy may be affected by the ambient temperature and usage time, so corresponding corrections are required. Organize the calculated luminous flux parameters into a data table to ensure the structuring of information. The table should include the timestamp, power, luminous efficacy, and calculated luminous flux values for subsequent analysis and mining. Determine the calculation methods for the color temperature and color rendering index (CRI) of the light source. The color temperature is usually in Kelvin (K) and can be calculated through color coordinates (such as the CIE1931 chromaticity diagram). The color rendering index is usually determined by the spectral distribution of the light source, and a standard light source (such as D65) is used for comparison to calculate the color rendering ability of the light source. Extract the spectral data from the historical light source operating status parameters (such as measured by a spectrophotometer), and calculate the color temperature and color rendering index based on the spectral data. The color temperature calculation formula or chromaticity diagram software can be used for calculation to ensure the accuracy of the results. Record the color temperature and color rendering index values at each time point and associate them with other operating parameters of the light source (such as power, temperature) for subsequent analysis. Organize the calculated color temperature and color rendering index into a data table to ensure the structuring of information. The table should include the timestamp, light source power, color temperature, and color rendering index values for subsequent analysis and comparison.Select suitable feature mining methods, such as data mining algorithms (e.g., clustering analysis, principal component analysis (PCA)), to extract the light source state features from luminous flux, color temperature, and color rendering index. These features will reflect the working state and performance changes of the light source. Determine the objectives of feature mining, such as identifying the performance degradation patterns of the light source, the trend of color temperature change, etc. Different thresholds and criteria can be set for classification and identification. Apply the selected feature mining method to analyze the sorted luminous flux, color temperature, and color rendering index data to identify potential patterns and trends. For example, through clustering analysis, the working state of the light source can be divided into three categories: normal, degraded, and faulty. Record the parameter settings, algorithm selection, and results in each feature mining process to ensure the scientific nature and traceability of the analysis. Organize the mined light source state features into a data table to ensure that the information is clear and readable. The table should include the descriptions of different features, classification results, and their corresponding operating states for subsequent analysis and application. Generate a feature mining report, detailing the mining process and results, to provide a basis for subsequent light source state monitoring and optimization. Integrate the light source state features extracted from historical data to ensure the continuity and consistency of the data. Select an analysis period (e.g., daily, weekly, monthly) for data aggregation to observe long-term trends. Set a time interval (e.g., once a day) to record the state features at each time point for subsequent change evolution analysis. Use data visualization tools (e.g., Matplotlib, Excel) to construct the light source state change curve. According to the extracted state features (such as luminous flux, color temperature, color rendering index), plot the change curves of different features for intuitive trend observation. Mark key events (such as light source replacement, fault repair, etc.) in the curve to analyze the impact of these events on the light source state. Analyze the constructed light source state change curve to identify long-term trends and sudden changes, and determine the critical point of light source performance degradation. Conduct a comprehensive analysis in combination with external environmental factors (such as temperature, humidity) to provide a more comprehensive light source state assessment. Generate a long-term time series change analysis report, detailing the construction process, analysis results, and suggestions of the state change curve, to provide a basis for subsequent light source management and optimization.

[0023] 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: Perform multi-dimensional trend change mining of the long-term light source state change curve to generate trend change features of multiple light source parameters; Conduct light source output attenuation detection based on the trend change features of multiple light source parameters to obtain light source output attenuation data; Calculate the attenuation rate and amplitude of the light source output attenuation data; Conduct non-linear light source attenuation analysis based on the attenuation rate and amplitude to generate non-linear light source attenuation laws; Perform multi-period light source attenuation evolution according to the non-linear light source attenuation law to generate a light source attenuation evolution trajectory; Perform full-cycle attenuation evolution fitting on the light source attenuation evolution trajectory to construct a light source attenuation evolution model.

[0024] In this embodiment, long-time series data on the change of the light source state is extracted from historical light source operation state parameters and data such as luminous flux, color temperature, and color rendering index. These data should include the light source output parameters in different time periods, ensuring a sufficient time span (such as several months or years) for trend analysis. Integrate different light source output parameters into the same data table to ensure that there are corresponding luminous flux, color temperature, color rendering index, etc. data for each time point. Each data point should contain information such as time stamp, luminous flux (lumens), color temperature (Kelvin), color rendering index (CRI), etc. Select suitable analysis methods, such as time series analysis, moving average method, and linear regression, etc., to mine the trend change characteristics of each light source parameter from the integrated data. These characteristics will provide a basis for subsequent attenuation detection. Multiple time windows (such as daily, weekly, monthly) can be set for trend change analysis to facilitate observing the change characteristics at different time scales. According to the extracted luminous flux data, set the criteria for attenuation detection. For example, it can be defined that if the luminous flux drops by more than 10% within a specific time period, it is considered attenuation. By calculating the moving average value of the luminous flux, identify the time points where the luminous flux drops significantly and mark them as attenuation events. Count the occurrence frequency of the attenuation events and the corresponding time periods, and record the specific values and times of each attenuation. For the marked attenuation events, extract the data on the attenuation of the light source output, including the luminous flux values, color temperature, and color rendering index, etc. before and after the attenuation. These data will provide a basis for subsequent calculation of the attenuation rate. Organize the attenuation data to generate a database of attenuation events, ensuring that the information is structured for subsequent analysis. According to the extracted data on the attenuation of the light source output, calculate the attenuation rate. The attenuation rate is defined as the change in luminous flux divided by the time unit. The formula can be used: R = ΔΦ / Δt, where R is the attenuation rate, ΔΦ is the change in luminous flux, and Δt is the change in time. Calculate for each attenuation event and record the attenuation rate of each event for subsequent analysis. The attenuation amplitude can be defined as the difference between the luminous flux before attenuation and the luminous flux after attenuation. Record the amplitude of each attenuation event to provide a basis for subsequent non-linear analysis. A = Φ(before) - Φ(after), where A is the attenuation amplitude, and Φ(before) and Φ(after) are the luminous fluxes before and after attenuation respectively. Select suitable non-linear analysis methods, such as polynomial regression, curve fitting, or neural network models, to model the attenuation rate and amplitude. Non-linear analysis can capture the complex behavior patterns during the light source attenuation process. Set model parameters, such as the order of the polynomial or the number of layers of the neural network, for subsequent analysis. Use the selected non-linear analysis method to analyze the organized attenuation data and generate the law of light source attenuation. By fitting the observed attenuation rate and amplitude data, obtain a mathematical model describing the attenuation characteristics of the light source. Record the goodness of fit (such as R² value) of each model to evaluate the effectiveness and accuracy of the model. Organize the results of the non-linear analysis into a data table to ensure that the information is clear and readable.Generate a report that details the analysis process and results of the non-linear attenuation law, providing a basis for subsequent light source management. Based on the generated non-linear light source attenuation law, conduct a multi-period light source attenuation evolution analysis. Set multiple time nodes to simulate the attenuation of the light source at different time periods to observe the trend of attenuation evolution. Record the luminous flux, color temperature, and color rendering index at different time nodes for easy analysis of their changes. Integrate the multi-period attenuation data obtained from the simulation into a light source attenuation evolution trajectory diagram. By plotting a time series diagram, visually display the attenuation of the light source output at different time periods. Mark important events (such as maintenance, replacement) on the trajectory diagram to facilitate analysis of the impact of these events on light source attenuation. Select an appropriate fitting method, such as the least squares method or Bayesian regression, to perform a full-cycle fit on the light source attenuation evolution trajectory. Ensure that the selected method can effectively capture the changing characteristics during the attenuation process. Determine the parameters of the fitting model, such as the reference point, fitting period, etc., to ensure the accuracy of the fit. Perform a full-cycle fit on the light source attenuation evolution trajectory, record the parameter changes and results during the fitting process. Evaluate the effectiveness of the fitting model and make adjustments by comparing the deviation between the fitting result and the actual data. Generate a fitting model, record the parameters and goodness of fit of the model to ensure the traceability of the results. Organize the fitting results into a data table to ensure clear and readable information. Generate a report that details the fitting process and results of the light source attenuation evolution model, providing a basis for subsequent light source management and optimization.

[0025] In this embodiment, refer to Figure 4 , which is a schematic diagram of the detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Extract real-time light source operation status monitoring parameters based on the light source monitoring probe; Input the real-time light source operation status monitoring parameters into the light source attenuation evolution model to conduct a light source attenuation evolution simulation and generate light source attenuation evolution simulation data; Conduct a future multi-time-point rolling light attenuation prediction on the light source attenuation evolution simulation data to generate light source parameter attenuation values at multiple time points; Conduct a light source attenuation gradient trend analysis based on the light source parameter attenuation values at multiple time points to obtain the light source attenuation gradient trend.

[0026] In this embodiment, ensure that the light source monitoring probes (such as optical sensors, temperature sensors, and current sensors) are correctly installed and connected to the data acquisition system. The monitoring probes should have high sensitivity and high precision and be able to record the operating state parameters of the light source in real time. Configure the data acquisition system, set the sampling frequency (for example, sample once per second), and ensure that it can capture the rapid changes of the light source. Set a reasonable monitoring range, such as the light intensity between 0 and 1000 lumens and the current between 0 and 5 amperes. Start the light source monitoring probes and begin to collect the operating state parameters of the light source in real time, including luminous flux (lumens), operating voltage (volts), operating current (amperes), temperature (degrees Celsius), etc. Ensure that the collection is carried out under different working conditions to cover all operating states of the light source. Record the timestamps of each parameter for subsequent analysis. Ensure that external interferences, such as strong light sources or temperature changes, are avoided during the collection process to improve the accuracy of the data. Store the real-time monitored operating state parameters of the light source in a high-capacity database in a lossless format (such as CSV or SQL) to ensure the integrity of the data. Conduct a preliminary cleaning of the data to remove outliers and missing values to ensure the reliability of subsequent analysis. Select a suitable light source attenuation evolution model based on previous research and data, such as an exponential decay model or a non-linear regression model. These models can effectively simulate the attenuation process of the light source and reflect the influence of different operating states on attenuation. Determine the key parameters of the model, such as the initial luminous flux, attenuation rate, and time constant. Preliminary estimation can be carried out through historical data to ensure the rationality of the model. Input the extracted real-time monitored operating state parameters of the light source into the constructed attenuation evolution model. Ensure that the monitoring data at each time point can be effectively utilized to update the state of the model. For example, the real-time luminous flux and current can be used to adjust the initial parameters and attenuation rate in the model. Record the timestamps and parameter values of the input data for subsequent simulation and analysis. Run the model for light source attenuation evolution simulation, using the real-time monitored parameters as inputs to generate the attenuation data of the light source over a period of time in the future. The simulation results should include the predicted values of the luminous flux at different time points. Adopt rolling prediction techniques to predict the light source attenuation values at multiple future time points. Time series prediction methods, such as the ARIMA model or the moving average method, can be used, combined with the previous attenuation evolution simulation data for prediction. Set the prediction time range, such as the next 7 days or 30 days, to ensure that enough time is covered to observe the attenuation trend. Based on the attenuation evolution simulation data, gradually calculate the attenuation values of the light source parameters at multiple future time points. For example, starting from the current moment, predict at fixed time intervals (such as daily), and record parameters such as the luminous flux, color temperature, and color rendering index at each time point. Store the results of each prediction in the database to ensure the structuring and traceability of the information. Define the light source attenuation gradient as the combination of the attenuation rate and amplitude of the light source parameters over a period of time. For example, the change rate of the luminous flux (such as the daily attenuation amount) can be used to represent the attenuation gradient.Analyze the calculated attenuation gradient to identify changes in the attenuation trend. Statistical analysis methods, such as the moving average method, can be used to smooth the data to eliminate short-term fluctuations and extract the long-term trend. Combine external environmental factors (such as temperature, humidity, etc.) and analyze their impact on the light source attenuation gradient to identify potential influencing factors. Organize the analysis results of the light source attenuation gradient, generate a report, and record in detail the attenuation gradient and its change trend at each time point to provide a basis for subsequent light source management and optimization. Include charts in the report to visually display the changes in the attenuation gradient, facilitating understanding and decision-making by relevant personnel.

[0027] In this embodiment, step S4 includes the following steps: Obtain the real-time working current and voltage parameters of the automated light source; identify the environmental light signal of the automated light source; Conduct an analysis of the circuit characteristics in the operating state based on the real-time working current and voltage parameters to generate the circuit state characteristics in the operating state; Perform pre-compensation calculation for light source attenuation based on the light source attenuation gradient trend to generate a pre-compensation value for light attenuation; Analyze the light attenuation compensation requirements of the drive circuit for the circuit state characteristics in the operating state according to the pre-compensation value for light attenuation to obtain the drive circuit light attenuation compensation requirement parameters; Perform adaptive light attenuation compensation optimization based on the drive circuit light attenuation compensation requirement parameters to generate an adaptive light attenuation compensation optimization engine.

[0028] In this embodiment, ensure that the current and voltage monitoring devices of the automated light source (such as current sensors and voltage sensors) are correctly installed and connected to the data acquisition system. These sensors should have high sensitivity and accuracy to monitor the operating status of the light source in real time. Configure the data acquisition system and set a reasonable sampling frequency (for example, sample once per second) to ensure that rapid changes in current and voltage can be captured. The monitoring range can be set, for example, the voltage is between 0 and 240 volts, and the current is between 0 and 5 amperes. Start the current and voltage monitoring devices to start real-time acquisition of the operating current and voltage parameters of the automated light source. Record the current and voltage values at each time point and attach a time stamp for subsequent analysis. During the acquisition process, ensure that the device is monitored under normal operating conditions to avoid external interference (such as electromagnetic interference) affecting the accuracy of the data. Install an ambient light sensor to ensure that its position can effectively monitor the ambient light intensity. The sensor should have a real-time monitoring function and be able to record changes in ambient light. Configure the sampling frequency of the sensor to be consistent with the current and voltage monitoring system to ensure data synchronization. Start the light sensor to start real-time acquisition of the ambient light signal, record the light intensity (such as in lux) and its change trend. Ensure that data is collected at different time periods to cover all states of light changes. Record the light intensity value and the corresponding time stamp at each time point for subsequent data analysis. Integrate the collected ambient light data with the real-time current and voltage parameters to form a comprehensive data table. Ensure that the current, voltage, and light intensity at each time point can be corresponding to each other for subsequent analysis. Generate an analysis report of the ambient light signal, record the change trend of the light intensity and its impact on the operating status of the automated light source, and provide a basis for subsequent circuit characteristic analysis. Select suitable circuit analysis methods, such as impedance analysis and power analysis, to evaluate the operating circuit characteristics of the automated light source. Through these analyses, the performance of the circuit under different operating conditions can be understood. Set analysis objectives, such as determining the efficiency, power factor, and load capacity of the circuit, etc., for subsequent characteristic evaluation. Use the real-time acquired current and voltage parameters to perform circuit characteristic analysis. For example, the power of the circuit can be calculated (P = V × I), and its power factor (PF = P / (V × I)) can be evaluated to judge the effectiveness of the circuit. Record the parameters and calculation results of each circuit state during the analysis process to ensure the accuracy and traceability of the data. Based on the calculated attenuation gradient, perform pre-compensation calculation for light source attenuation. The compensation value can be calculated through a set compensation strategy, such as setting the corresponding current or voltage adjustment amplitude according to the attenuation rate. Set a compensation coefficient (such as 1.2 times the attenuation gradient) for real-time dynamic adjustment according to the actual light attenuation situation. According to the pre-calculated light attenuation compensation value, define the light attenuation compensation requirement parameters of the drive circuit. These parameters should include the required current adjustment amount, voltage adjustment amount, and their corresponding time tags. Determine the compensation requirement at each time point for subsequent analysis.Analyze the difference between the current state and the required state of the drive circuit, and evaluate its compensation requirements. For example, the amount of compensation to be applied can be determined by calculating the difference between the current current and the target current. Record the compensation requirement parameters at each time point to ensure the accuracy and traceability of the data. Select a suitable adaptive compensation optimization method, such as fuzzy logic control, adaptive filters, or machine learning algorithms, to achieve dynamic optical attenuation compensation. Determine the optimization goal, such as minimizing the attenuation amplitude of the light flux, to ensure the stability and efficiency of the light source. Input the current and voltage parameters and compensation requirement parameters monitored in real time into the adaptive optimization model to dynamically adjust the output of the drive circuit. For example, the current value can be adjusted according to the real-time difference to achieve the required optical attenuation compensation. Record the process and results of each optimization to ensure the effectiveness and accuracy of each adjustment. Organize the results of the adaptive compensation optimization into a data table to ensure the structuring of the information. Generate an optimization report, detailing the process, parameters used, and results of the compensation optimization, to provide a basis for subsequent light source management and performance improvement.

[0029] In this embodiment, the specific steps for analyzing the optical attenuation compensation requirements of the drive circuit for the operating state circuit state characteristics according to the pre-compensation value of optical attenuation to obtain the drive circuit optical attenuation compensation requirement parameters are as follows: Compare the multi-state parameter differences of the operating state circuit state characteristics according to the pre-compensation value of optical attenuation to obtain the degree of compensation value difference; Analyze the change trend of the degree of compensation value difference; Identify the trend direction of the change trend to extract the change direction of the difference trend; Calculate the amplitude of the change trend; Conduct an analysis of the optical attenuation compensation requirements of the circuit based on the change direction of the difference trend and the amplitude to obtain the drive circuit optical attenuation compensation requirement parameters.

[0030] In this embodiment, real-time operating state circuit state characteristic data are collected, including current, voltage, luminous flux, and pre-compensation value of light attenuation. These data should cover multiple time periods for comparative analysis of multiple states. Ensure that each data point has a timestamp for subsequent analysis. Organize these data into a structured data table to ensure that the parameters of each state can be clearly corresponding to the corresponding time points. This provides a basis for subsequent difference comparison. For each time point, calculate the difference between the current state characteristic and the pre-compensation value. D = C - P, where D is the difference value, C is the current state characteristic parameter, and P is the pre-compensation value of light attenuation. Generate a change trend graph of the difference value to visually display the change of the compensation value difference degree over time. Through visualization means, identify the fluctuation of the difference value for subsequent trend analysis. According to the sorted difference value data, calculate the change trend of the difference degree. The moving average method (such as the 7-day moving average) can be used to smooth the data to eliminate short-term fluctuations and highlight long-term trends. Calculate the slope of the change trend to determine whether the difference degree is increasing, decreasing, or remaining stable. The calculation of the slope can use the linear regression method to ensure the reliability of the results. Organize the results of the change trend into a data table, recording the difference degree and its corresponding change trend for each time period. Ensure that the information is structured for subsequent trend direction identification. Generate a trend analysis report, detailing the calculation process, parameters used, and results of the change trend, providing a basis for subsequent trend direction identification. Define the direction of the change trend as increasing, decreasing, or stable. By analyzing the slope of the change trend, judge the trend direction. For example, if the slope is positive, it indicates an upward trend; if it is negative, it indicates a downward trend. Record the trend direction for each time period for subsequent analysis. The second difference method is used to detect changes in the trend direction. Calculate the change rate for each time period, and then judge the positive and negative values of these change rates to determine changes in the trend direction. According to the calculation results, mark the change points of the trend direction for further analysis. Organize the results of the trend direction identification into a data table to ensure clear readability of the information. Generate a trend direction analysis report, detailing the trend direction and its changes for each time period, providing a basis for subsequent compensation requirement analysis. Define the amplitude of the change trend as the absolute value of the change in the difference degree, A = |D(current) - D(previous)|, where A is the amplitude, D(current) is the difference value at the current time point, and D(previous) is the difference value at the previous time point. Organize the calculated amplitude data into a data table to ensure the structuring of the information. Generate an amplitude change curve graph to visually display the change of the amplitude for analysis. According to the difference trend change direction and amplitude, define the light attenuation compensation requirement parameters for the drive circuit. These parameters should include the required current and voltage adjustment amounts, as well as the corresponding time tags. A compensation coefficient can be set for dynamic adjustment according to the actual difference situation.Analyze the differences between the current circuit state and the required state, and evaluate its compensation requirements. For example, if the difference trend is upward and the amplitude is large, it may be necessary to increase the current output to maintain the brightness of the light source. Record the compensation requirement parameters at each time point to ensure the accuracy and traceability of the data.

[0031] In this embodiment, the specific steps of step S5 are as follows: Calculate the light intensity of the environmental light signal to obtain the environmental light intensity value; Track the spatial distribution of light rays according to the environmental light signal to generate the spatial distribution characteristics of light rays; Mine the temporal changes of environmental light according to the environmental light intensity value and the spatial distribution characteristics of light rays to generate the temporal change characteristics of environmental light; Model the light feature trend of the temporal change characteristics of environmental light to construct the environmental light change trend map.

[0032] In this embodiment, a light sensor (such as a photoelectric sensor or a light intensity meter) is installed to monitor the ambient light signal in real time. Ensure that the sensor can work properly under different light conditions and has sufficient sensitivity and accuracy. It is recommended to use a sensor with a range of 0 - 1000 lux. Configure a data acquisition system and set a reasonable sampling frequency (such as once per second) to capture the changes in light intensity. Ensure a stable connection between the sensor and the data acquisition system to reduce the risk of data loss. Start the light sensor to begin real-time acquisition of the ambient light signal. Record the light intensity (in lux) at each time point and attach a timestamp for subsequent analysis. During the acquisition process, ensure that the sensor is in a stable environment to avoid interference from strong light sources or reflected light on the measurement results, thereby improving the accuracy of the data. Convert the acquired light signal into a light intensity value through a calculation formula. The light intensity value can be directly obtained from the output of the sensor without additional calculation, but the sensor needs to be calibrated to ensure accuracy. Select a suitable ray tracing model, such as the ray tracing algorithm (Ray Tracing) or the radiative transfer method (Radiative Transfer), to simulate the propagation and distribution of light in space. The selected model should be able to accurately capture the directionality of the light source and the reflection characteristics of the environment. Determine the parameter settings for ray tracing, such as the number of rays, the tracing depth, and the handling methods for reflection and refraction, to ensure the effectiveness of the model. Based on the real-time acquired ambient light signal, start the ray tracing simulation. Use the light intensity and direction of the light source as inputs to simulate the propagation path of light in space, and record the distribution characteristics after the light interacts with objects. Generate a distribution map of light in space, record the incident angle, reflection angle of each ray and their corresponding light intensity for subsequent analysis. Integrate the real-time light intensity value with the light spatial distribution characteristics to form a comprehensive time series dataset. These data should include timestamps, light intensity, and their spatial distribution characteristics for subsequent time series change analysis. Ensure the time alignment of the data, that is, the light intensity and the light distribution characteristics at each time point can correspond to each other to ensure the continuity of the analysis. Adopt time series analysis methods, such as autocorrelation analysis and moving average method, to explore the characteristics of the ambient light intensity changing over time. These characteristics can include fluctuations in light intensity, periodic changes, and sudden changes, etc. Record the change trend of light intensity in each time period and extract important time series change characteristics, such as the maximum light intensity, the minimum light intensity, and their change rates. Select a suitable modeling method, such as the state space model (StateSpaceModel) or a time series prediction model (such as ARIMA), to model the ambient light changes. Ensure that the selected model can effectively capture the time series characteristics and change rules of the light intensity. Set the key parameters of the model, such as the model order and the delay time, to ensure the accuracy and effectiveness of the modeling. Conduct a situation modeling based on the sorted ambient light time series change characteristics.Take the light intensity and its change characteristics as input, train the model to capture the change trend and pattern of the light intensity. Record the parameter settings and results during the modeling process to ensure the traceability and accuracy of the model. Visualize the modeling results to generate an environmental light change situation map. The map should show the change trend of the light intensity over time, as well as important change nodes and patterns for intuitive analysis. Generate a situation map analysis report, detailing the modeling process, results, and their interpretation of the environmental light change, providing a basis for subsequent light source management and optimization.

[0033] In this embodiment, the specific steps of step S6 are as follows: Conduct an optimal environmental light demand analysis based on the environmental light change situation map to generate optimal environmental light demand characteristics; Adjust the light attenuation compensation intensity based on the optimal environmental light demand characteristics to obtain an environmental change light attenuation compensation intensity adjustment value; Perform dynamic light attenuation compensation fine-tuning on the adaptive light attenuation compensation optimization engine based on the environmental change light attenuation compensation intensity adjustment value to construct a dynamic light attenuation compensation control model.

[0034] In this embodiment, the light intensity data and its corresponding timestamps included in the ambient light change trend map are collected. These data should cover different environmental conditions and time periods to facilitate a comprehensive analysis of lighting requirements. The data is organized into a structured table, including information such as light intensity, time period, and light change trend. Ensure that each data point can clearly reflect the change characteristics of the ambient light. Select appropriate demand analysis methods, such as clustering analysis or optimization algorithms, to identify the optimal ambient light demand characteristics. These methods can help us find the most suitable light intensity levels under different environmental conditions. Set the analysis objectives, such as determining the minimum and maximum light intensities, to ensure that the lighting in the environment can meet the functional requirements without causing waste. According to the generated optimal ambient light demand characteristics, define the light decay compensation intensity adjustment parameters. These parameters include the current light intensity, the optimal light intensity, and the required compensation amount. Set the compensation strategy, such as increasing the output when the ambient light intensity is lower than the optimal value and decreasing the output when the light intensity is higher than the optimal value, to ensure that the light source always maintains the best state. Calculate the light decay compensation intensity adjustment value, A = O - N, where A is the compensation intensity adjustment value, O is the current light intensity, and N is the optimal light intensity. If A is positive, the light source output needs to be increased; if it is negative, the output needs to be decreased. Design a dynamic light decay compensation control model and select a suitable control strategy (such as PID control or fuzzy control) to adjust the light source output. The model should be able to respond to ambient light changes in real time and optimize the output according to the adjustment value. Determine the key parameters of the model, such as response time, stability, and accuracy, to ensure the effectiveness of the control model. Input the ambient change light decay compensation intensity adjustment value into the dynamic light decay compensation control model and fine-tune it according to the real-time monitored ambient light signal. For example, when the light intensity is lower than the optimal value, the model will automatically increase the output current or voltage of the light source to achieve compensation. Record the process and effect of each fine-tuning to ensure the effectiveness and accuracy of each adjustment. Organize the adjustment results of the dynamic light decay compensation control model into a data table to ensure the structuring of information. Conduct model verification, compare the deviation between the output light intensity and the optimal light intensity through experimental data, and evaluate the performance of the model. Generate a report, detailing the establishment process, adjustment effect of the dynamic compensation control model, and its significance for ambient light management, providing a basis for subsequent optimization.

[0035] In this embodiment, a control system is provided for implementing the automated light source control method described above, including: A light source status module, used to obtain historical light source operation status parameters, perform long-time series operation status change evolution, and construct a long-time series light source status change curve; A non-linear light decay analysis module, used to perform non-linear light source attenuation analysis and multi-period light source attenuation evolution on the long-time series light source status change curve to construct a light source attenuation evolution model; The light attenuation evolution simulation module is used to obtain the real-time monitoring parameters of the light source operation state and the ambient light signal; and input them into the light source attenuation evolution model for light source attenuation evolution simulation, so as to obtain the light source attenuation gradient trend; The adaptive light attenuation compensation module is used to obtain the real-time working current and voltage parameters of the automatic light source, and perform adaptive light attenuation compensation optimization based on the light source attenuation gradient trend, so as to generate an adaptive light attenuation compensation optimization engine; The ambient light change module is used to mine the ambient light time series change of the ambient light signal, and perform light feature trend modeling to construct an ambient light change trend map; The light attenuation compensation control module is used to perform dynamic light attenuation compensation fine-tuning on the adaptive light attenuation compensation optimization engine according to the ambient light change trend map, and construct a dynamic light attenuation compensation control model.

[0036] Through the accumulation of long-time series data, the present invention can accurately track the whole process of the light source from the start of use to attenuation, reflecting the trend of state changes of the light source at different stages. The light source state module can provide a scientific basis for subsequent attenuation analysis, timely identify potential decline risks, ensure the early detection of abnormal states, and reduce the performance degradation or failure of the light source caused by excessive attenuation. It provides accurate historical data support for subsequent light attenuation evolution modeling and compensation algorithms, enhancing the accuracy and reliability of the system. Through non-linear analysis, it can handle the complex attenuation characteristics of actual light sources, which may not only change linearly over time but also be non-linearly affected by factors such as the environment and usage frequency, ensuring more accurate attenuation prediction. By analyzing the attenuation data in multiple time periods, it can identify the decline characteristics of the light source under different usage conditions, further improving the flexibility and adaptability of the prediction. Based on the output of this model, personalized attenuation compensation schemes can be set for different light sources, achieving efficient attenuation prediction and adjustment. By real-time input of the light source operating state and ambient light data, it can dynamically simulate the decline process of the light source. Compared with static analysis, this real-time simulation can better cope with the impacts brought by the changes in the light source state and environment. Through the simulated light source attenuation gradient trend, the rate and amplitude of attenuation can be clarified, providing high-precision input for subsequent compensation. It provides real-time feedback for the adaptive light attenuation compensation module, helping the system better understand the current state of the light source, and thus making more accurate adjustments. Through the adaptive compensation strategy, parameters such as current, voltage, and temperature can be adjusted according to real-time data to optimize the working state of the light source and offset the brightness and performance degradation caused by decline. According to different light source types, working conditions, and environmental changes, the system can flexibly adjust the compensation strategy to ensure long-term stable light source performance. Adaptive compensation can avoid over-compensation, save energy, extend the light source life, and at the same time maintain the stability and efficiency of the light source output. It can identify and capture environmental light changes (such as the changes between day and night, different seasons or weather conditions, etc.), ensuring that the light source can adjust its output according to environmental conditions and avoiding the deviation of light source performance under the influence of environmental light. Through in-depth analysis of the ambient light characteristics, an accurate ambient light change trend map can be constructed, providing accurate data support for the environmental adaptability adjustment of the light source. By dynamically adjusting the light source output and compensating according to real-time ambient light changes, it ensures that the light source always maintains the optimal lighting effect, while avoiding over-compensation or under-compensation. Dynamically fine-tuning the compensation intensity enables the system to make subtle and accurate adjustments according to the changes in the environment and light source state, ensuring the stability of parameters such as the brightness and color temperature of the light source. By constructing a dynamic light attenuation compensation control model, it can not only adjust the light source in real time but also automatically adjust the light source compensation intensity according to environmental changes, achieving global light source optimization.

[0037] Therefore, from any perspective, the embodiments should be regarded as exemplary and non-limiting. The scope of the present invention is defined by the appended claims rather than the above description. Therefore, 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.

[0038] As described above, these are merely 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 invented herein.

Claims

1. An automatic light source control method, characterized in that: The following steps are involved: Step S1: Obtain historical light source operating state parameters, perform long-term operating state change evolution, and construct a long-term light source state change curve; Step S2: performing nonlinear light source attenuation analysis and multi-period light source attenuation evolution on the long-time sequence light source state change curve to construct a light source attenuation evolution model; Step S3: Acquire real-time light source operation status monitoring parameters and ambient light signals; And input it into the light source attenuation evolution model to simulate the light source attenuation evolution, so as to obtain the light source attenuation gradient situation; Step S4: acquiring the real-time working current and voltage parameters of the automatic light source, and performing adaptive light attenuation compensation optimization based on the light source attenuation gradient situation, thereby generating an adaptive light attenuation compensation optimization engine; Step S5: mining the ambient light time series changes of the ambient light signal, and performing light feature situation modeling to construct an ambient light change situation map; Step S6: fine-tuning the dynamic light attenuation compensation of the adaptive light attenuation compensation optimization engine according to the ambient light change trend diagram, and constructing a dynamic light attenuation compensation control model.

2. The automatic light source control method according to claim 1, characterized in that: The specific steps of step S1 are: Get historical light source operating status parameters; Calculate the light source output luminous flux according to the historical light source operation status parameters and extract the historical light source luminous flux parameters; Analyze light source color temperature and color rendering index based on historical light source operating status parameters; Mining the light source state characteristics based on historical light source luminous flux parameters, light source color temperature, and color rendering index to generate light source state characteristics; The long-term running state change evolution of the light source state characteristics is carried out to construct a long-term light source state change curve.

3. The automatic light source control method according to claim 1, characterized in that: The specific steps of step S2 are: Perform multi-dimensional light source output parameter trend change mining on the long-term light source state change curve to generate trend change characteristics of multiple light source parameters; Performing light source output attenuation detection according to trend change characteristics of multiple light source parameters to obtain light source output attenuation data; Calculate the attenuation rate and amplitude of the attenuation data output by the light source; Perform nonlinear light source attenuation analysis according to the attenuation rate and amplitude to generate a nonlinear light source attenuation law; According to the nonlinear light source attenuation law, the light source attenuation evolution is carried out in multiple time periods to generate the light source attenuation evolution trajectory; The full-cycle attenuation evolution fitting is performed on the light source attenuation evolution trajectory to construct the light source attenuation evolution model.

4. The automatic light source control method according to claim 1, characterized in that: The specific steps of step S3 are: Extract real-time light source operation status monitoring parameters based on light source monitoring probe; Inputting the real-time light source operation status monitoring parameters into the light source attenuation evolution model, performing light source attenuation evolution simulation, and generating light source attenuation evolution simulation data; Perform rolling light attenuation predictions at multiple time points in the future on the light source attenuation evolution simulation data to generate light source parameter attenuation values ​​at multiple time points; The light source attenuation gradient situation analysis is performed according to the attenuation values ​​of the light source parameters at multiple time points, so as to obtain the light source attenuation gradient situation.

5. The automatic light source control method according to claim 1, characterized in that: The specific steps of step S4 are: Acquiring real-time operating current and voltage parameters of the automated light source; identifying ambient light signals of the automated light source; Performing an operating circuit characteristic analysis according to the real-time working current and voltage parameters to generate operating circuit state characteristics; Perform light source attenuation pre-compensation calculation based on the light source attenuation gradient situation to generate a light attenuation pre-compensation value; According to the light attenuation pre-compensation value, the light attenuation compensation requirement of the driving circuit is analyzed based on the state characteristics of the running circuit to obtain the light attenuation compensation requirement parameters of the driving circuit; Adaptive light attenuation compensation optimization is performed based on the light attenuation compensation requirement parameters of the driving circuit, thereby generating an adaptive light attenuation compensation optimization engine.

6. The automatic light source control method according to claim 5, characterized in that: The specific steps of analyzing the light attenuation compensation requirement of the driving circuit according to the light attenuation pre-compensation value on the running circuit state characteristics to obtain the light attenuation compensation requirement parameters of the driving circuit are: According to the optical attenuation pre-compensation value, a multi-state parameter difference comparison is performed on the state characteristics of the running circuit to obtain the degree of difference in the compensation value; Analyzing the variation trend of the difference degree of the compensation values; Performing trend direction identification on the change trend to extract the difference trend change direction; Calculating the magnitude of the changing trend; According to the change direction of the difference trend and the amplitude, the circuit light attenuation compensation demand analysis is performed to obtain the light attenuation compensation demand parameters of the driving circuit.

7. The automatic light source control method according to claim 1, characterized in that: The specific steps of step S5 are: Performing light intensity calculation on the ambient light signal to obtain an ambient light intensity value; Tracing the spatial distribution of light according to the ambient light signal to generate a spatial distribution feature of light; Mining the time series changes of ambient light based on the ambient light intensity value and the spatial distribution characteristics of light, and generating the time series change characteristics of ambient light; The light characteristic situation modeling is performed on the time-series change characteristics of the ambient light to construct the ambient light change situation map.

8. The automatic light source control method according to claim 1, characterized in that: The specific steps of step S6 are: Perform optimal ambient light demand analysis based on the ambient light change trend diagram to generate optimal ambient light demand characteristics; Adjust the light attenuation compensation intensity based on the optimal ambient light demand characteristics to obtain an adjustment value of the light attenuation compensation intensity for environmental changes; Based on the light attenuation compensation intensity adjustment value of environmental changes, the adaptive light attenuation compensation optimization engine is dynamically adjusted to construct a dynamic light attenuation compensation control model.

9. A control system, characterized in that: Used to execute the automatic light source control method as claimed in claim 1, comprising: The light source status module is used to obtain historical light source operating status parameters, perform long-term operating status change evolution, and construct a long-term light source status change curve; The nonlinear light decay analysis module is used to perform nonlinear light decay analysis on the long-term light source state change curve and the light source decay evolution in multiple time periods to build a light source decay evolution model; The light attenuation evolution simulation module is used to obtain real-time light source operation status monitoring parameters and ambient light signals; and input them into the light source attenuation evolution model to simulate the light source attenuation evolution, thereby obtaining the light source attenuation gradient situation; An adaptive light attenuation compensation module, used to obtain the real-time working current and voltage parameters of the automatic light source, and to perform adaptive light attenuation compensation optimization based on the light source attenuation gradient situation, thereby generating an adaptive light attenuation compensation optimization engine; An ambient light change module is used to mine the ambient light time series changes of the ambient light signal, perform light feature situation modeling, and construct an ambient light change situation map; The light decay compensation control module is used to fine-tune the dynamic light decay compensation of the adaptive light decay compensation optimization engine according to the ambient light change situation diagram, and to build a dynamic light decay compensation control model.

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