An automated light source adjustment method and system based on real-time environmental changes

Abstract

The present application relates to the technical field of light source adjustment, and particularly relates to an automatic light source adjustment method and system based on real-time environmental changes. The method comprises the following steps: collecting environmental light monitoring parameters of the automatic light source, and performing real-time light path tracking and dynamic light environment perception modeling to construct a real-time light environment perception map; obtaining a historical operation report of the automatic light source, performing light source parameter preference mining, and performing adaptive light source adjustment according to the real-time light environment perception map to construct an initial light source regulation strategy; performing light source control execution according to the initial light source regulation strategy, and performing spatial light source disturbance detection to generate multi-dimensional features of the disturbed light source; performing light source change trend evolution analysis on the multi-dimensional features of the disturbed light source, and performing intelligent light disturbance reverse compensation to construct an intelligent anti-interference light source regulation strategy. The present application performs real-time light source compensation adjustment according to environmental light source changes, and realizes accurate and rapid user light source demand response.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of light source adjustment, and in particular to an automatic light source adjustment method and system based on real-time environmental changes. BACKGROUND

[0002] With the continuous improvement of intelligent technology and the comfort requirements of living environment, lighting systems, as an important part of building spaces, industrial sites, smart homes and vehicle systems, are gradually developing towards automation and intelligence. Especially under the promotion of concepts such as green buildings, energy conservation and environmental protection, and smart cities, light source adjustment systems not only meet people's lighting needs, but also are endowed with higher energy saving, responsiveness and adaptability requirements. The traditional manual control of light source brightness and color temperature has been difficult to meet the precise lighting needs in dynamic and changing environments.

[0003] In actual application environments, the intensity of light, the color and distribution of light sources will fluctuate dynamically due to factors such as time changes, changes in external natural light conditions, indoor personnel activity states, and environmental reflection conditions. If the light source parameters cannot be adjusted in real time, it may lead to poor lighting effects, increased energy consumption, decreased user experience, and even affect the work efficiency and safety in specific scenarios such as medical, manufacturing, and display fields. Therefore, how to dynamically and intelligently control the state of the light source according to real-time environmental changes has become one of the key directions of lighting technology development.

[0004] At present, common light source adjustment systems are mostly based on timing settings, photosensitive elements or simple scene preset schemes to realize automatic adjustment of brightness and color temperature. These methods can improve the adaptability of light sources to some extent, but their adjustment methods are often single, with a lagging response, lack of perception ability for complex environmental changes, and difficult to achieve fine adjustment control. In addition, existing schemes usually cannot form a closed-loop feedback with the environmental state, lack of multi-dimensional data fusion and analysis support, and have weak response ability to sudden environmental changes or user behavior changes, making it difficult to achieve high-precision and high-efficiency automatic adjustment goals. SUMMARY

[0005] To solve the above technical problems, the present application provides an automatic light source adjustment method and system based on real-time environmental changes to solve at least one of the above technical problems.

[0006] To achieve the above purpose, the present application provides an automatic light source adjustment method based on real-time environmental changes, comprising the following steps:

[0007] Step S1: Collecting environmental lighting monitoring parameters of the automatic light source, and performing real-time light path tracking and dynamic light environment perception modeling to construct a real-time light environment perception map;

[0008] Step S2: Obtain the historical operation report of the automated light source, mine the light source parameter preferences, and perform adaptive light source adjustment according to the real-time light environment perception map, thereby constructing an initial light source regulation strategy;

[0009] Step S3: Perform light source control execution according to the initial light source regulation strategy, and conduct spatial light source disturbance detection to generate multi-dimensional features of the disturbed light source;

[0010] Step S4: Perform light source change trend evolution analysis on the multi-dimensional features of the disturbed light source, and conduct intelligent light disturbance reverse compensation to construct an intelligent anti-interference light source regulation strategy;

[0011] Step S5: Based on the intelligent anti-interference light source regulation strategy, perform dynamic light source parameter regulation and expected response speed delay calculation to obtain a light source response time delay value;

[0012] Step S6: Based on the light source response time delay value, make an adaptive error repair decision, and then perform dynamic feedback reinforcement learning to construct an intelligent light source feedback adjustment model.

[0013] In the present specification, an automated light source adjustment system based on real-time environmental changes is provided for performing the automated light source adjustment method based on real-time environmental changes as described above, comprising:

[0014] A light environment perception module for collecting environmental light monitoring parameters of the automated light source, and performing real-time light path tracking and dynamic light environment perception modeling to construct a real-time light environment perception map;

[0015] An adaptive light source adjustment module for obtaining the historical operation report of the automated light source, mining the light source parameter preferences, and performing adaptive light source adjustment according to the real-time light environment perception map, thereby constructing an initial light source regulation strategy;

[0016] A disturbance detection module for performing light source control execution according to the initial light source regulation strategy, and conducting spatial light source disturbance detection to generate multi-dimensional features of the disturbed light source;

[0017] An anti-interference regulation module for performing light source change trend evolution analysis on the multi-dimensional features of the disturbed light source, and conducting intelligent light disturbance reverse compensation to construct an intelligent anti-interference light source regulation strategy;

[0018] A time delay module for performing dynamic light source parameter regulation and expected response speed delay calculation based on the intelligent anti-interference light source regulation strategy to obtain a light source response time delay value;

[0019] A feedback adjustment module for making an adaptive error repair decision based on the light source response time delay value, and then performing dynamic feedback reinforcement learning to construct an intelligent light source feedback adjustment model.

[0020] The beneficial effects of the present application are as follows: by real-time acquisition of environmental lighting parameters such as illumination intensity, illumination direction, color temperature distribution, reflectivity, etc., the system can comprehensively perceive the current lighting state. The introduction of light path tracking technology can more accurately understand the propagation path of light in space and phenomena such as interference, refraction, and reflection by objects, thereby effectively avoiding blind spots and ghost areas. Dynamic light environment modeling not only helps to establish a real-time lighting perception map, but also provides dynamic data support for subsequent adaptive adjustment. Using historical operation data and current light environment perception results, preliminary intelligent light source strategy optimization is achieved. By analyzing the historical operation reports of automated light sources, the running preferences and response patterns of the light sources under different conditions can be mined, such as under what conditions the brightness adjustment is frequent, which lighting mode is more energy-efficient or more comfortable, etc. These preference information is a valuable asset for building an efficient light source control strategy. Combined with the real-time perception map, environmental adaptability adjustment can be performed to achieve preliminary adaptive optimization of light source brightness, color temperature, illumination angle, etc., thereby achieving energy saving and improving user experience. On the basis of executing the initial light source strategy, real-time monitoring of external or internal disturbance factors is introduced to provide protection for the system's anti-disturbance capability. Disturbance detection can identify abnormal phenomena such as obstructions entering the lighting range, sudden reflections, users temporarily changing light settings, etc. By generating multi-dimensional features (such as disturbance frequency, disturbance source direction, disturbance duration, disturbance light intensity change, etc.) for these disturbances, the system can better understand and distinguish between normal fluctuations and abnormal disturbances. The data of disturbance factors are converted into predictive and countermeasures to improve the system's autonomous response capability. Through comprehensive analysis of multi-dimensional disturbance features, the changing trends and laws of the lighting environment can be understood, and the occurrence and impact range of potential disturbances can be predicted. Combined with situation evolution analysis, the system can dynamically adjust the light compensation angle, brightness repair parameters, and response threshold, etc. to achieve disturbance compensation, effectively eliminating the lighting imbalance problems caused by obstructions, reflections, or local light source failures. The constructed anti-disturbance strategy has high adaptability and scene universality, and can maintain good lighting quality in various environments. When executing the anti-disturbance strategy, the system needs to constantly compare the time difference between the ideal response and the actual response to obtain the delay value of the light source response time. This delay value is of great significance for evaluating the system's execution efficiency, hardware performance bottlenecks, and network control delay. Through dynamic parameter regulation and delay modeling, the light source control path can be optimized, unnecessary intermediate processes can be reduced, and response accuracy can be improved. By introducing adaptive error repair and reinforcement learning mechanisms, closed-loop optimization is achieved. Based on the system errors identified by the delay value, adaptive repair algorithms can dynamically adjust the control logic or hardware scheduling parameters to effectively reduce the gap between the target output and the actual effect. Combined with dynamic feedback reinforcement learning, the system can learn and accumulate experience after each execution, continuously optimizing the strategy to make the light source control more and more accurate and the response more and more efficient.The finally constructed feedback regulation model can not only make optimal control decisions according to the current state, but also predict future states and make advanced responses, so that truly intelligent lighting management is realized. The model greatly improves the long-term operation stability and learning ability of the system, and has significant value for large-scale deployment of lighting systems in intelligent buildings and large-area offices. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 A step flow diagram of the automatic light source adjustment method based on real-time environmental changes of the application;

[0022] Figure 2 A detailed implementation step flow diagram of step S1;

[0023] Figure 3 A detailed implementation step flow diagram of step S2;

[0024] Figure 4 A detailed implementation step flow diagram of step S3. DETAILED DESCRIPTION

[0025] It should be understood that the specific embodiments described herein are only used to explain the application and do not limit the application.

[0026] The application provides an automatic light source adjustment method and system based on real-time environmental changes. The execution subject of the automatic light source adjustment method based on real-time environmental changes includes but is not limited to mechanical equipment, data processing platform, cloud server node, network upload equipment, etc. which can be regarded as general computing nodes of the application, and the data processing platform includes but is not limited to at least one of audio image management system, information management system and cloud data management system.

[0027] Please refer to Figures 1 to 4 The application provides an automatic light source adjustment method based on real-time environmental changes, which includes the following steps:

[0028] Step S1: Collect the environmental light monitoring parameters of the automatic light source, and perform real-time light path tracking and dynamic light environment perception modeling to construct a real-time light environment perception map;

[0029] Step S2: Obtain the historical operation report of the automatic light source, perform light source parameter preference mining, and perform adaptive light source adjustment according to the real-time light environment perception map, so as to construct an initial light source regulation strategy;

[0030] Step S3: Perform light source control execution according to the initial light source regulation strategy, and perform spatial light source disturbance detection to generate multi-dimensional features of the disturbed light source;

[0031] Step S4: light source change trend evolution analysis is performed on the multi-dimensional characteristics of the disturbed light source, intelligent light disturbance reverse compensation is performed, and an intelligent anti-interference light source regulation strategy is constructed;

[0032] Step S5: dynamic light source parameter regulation and expected response speed delay calculation are performed based on the intelligent anti-interference light source regulation strategy, so as to obtain a light source response time delay value;

[0033] Step S6: adaptive error repair decision is made based on the light source response time delay value, dynamic feedback reinforcement learning is performed, and an intelligent light source feedback adjustment model is constructed.

[0034] In the embodiment of the application, referring to Figure 1 The application is a kind of automatic light source adjustment method based on real-time environmental changes, and the steps of the method include:

[0035] Step S1: environmental light monitoring parameters of the automatic light source are collected, real-time light path tracking and dynamic light environment perception modeling are performed, and a real-time light environment perception map is constructed;

[0036] In this embodiment, environmental light monitoring parameters of automated light sources are collected to provide basic data for subsequent light path tracking and dynamic light environment perception modeling. Real-time monitoring of environmental light changes ensures that light source adjustments can adapt to different lighting conditions. Set experimental parameters, such as setting the light sampling frequency to 1 per second to obtain sufficient dynamic information. When implementing light monitoring, use high-precision photometers or light sensors (such as LDR or photoelectric sensors) to obtain the current environmental light intensity. Deploy light sensors at different locations to ensure comprehensive capture of environmental light changes. Connect the sensor to a data acquisition module (such as Arduino or Raspberry Pi) to read light data in real time. In specific implementation, first initialize the sensor and establish data connection. Then, use code to periodically read light intensity values and store them in data structures (such as arrays or lists). During monitoring, ensure that the sensor is in normal working condition and record any faults or data loss that may occur. The generated environmental light monitoring parameters will provide important basic data for subsequent light path tracking and dynamic light environment perception modeling, ensuring that light source adjustments can be optimized for actual lighting conditions. After successfully collecting environmental light monitoring parameters, real-time light path tracking is performed. Analyze the light path of the light source to ensure that the light can cover the required area and improve the efficiency of light source adjustment. Set experimental parameters, such as setting the light path tracking update frequency to 2 per second to ensure real-time data. When implementing light path tracking, first define the type of light source (such as point light source, parallel light source, etc.) and its lighting model. Use ray tracing algorithms (such as ray-based rendering techniques) to simulate the propagation of light in space. The ray tracing process can generate visual effects of light path by calculating the reflection, refraction, and shadow of light. In specific implementation, combine the data of the light sensor and use ray tracing algorithms in computer graphics to generate light paths. You can use Python's PyOpenGL or Pygame libraries to implement light rendering and path tracking. Each update, calculate the interaction of the light emitted by the current light source with the objects in the scene, record the light path and visualize it. The generated light path will provide intuitive visual information for dynamic light environment perception modeling, helping to judge the lighting effect and coverage of the light source. After completing the light path tracking, dynamic light environment perception modeling is performed. Based on real-time lighting data and light paths, a comprehensive light environment perception map is constructed to facilitate subsequent light source adjustment and optimization. Set experimental parameters, such as setting the modeling time window to 30 seconds to ensure data stability. When implementing light environment modeling, first collect the previously collected light monitoring parameters and light path data. Integrate these data into a data frame for subsequent analysis. You can use Python's Pandas library to integrate and process data.Smooth the collected lighting data using interpolation algorithms (such as linear interpolation or spline interpolation) and generate a light environment perception map. By visualizing the lighting data, create a two-dimensional or three-dimensional light environment map that shows the distribution of lighting intensity in different areas. During modeling, combine dynamic changes in the environment (such as moving objects or changes in light source position) to update the light environment map in real time. You can use Matplotlib or Plotly libraries to generate visual charts to visually display changes in lighting intensity. The final dynamic light environment perception map will provide important information support for automated light source adjustment, ensuring that the light source can be adjusted according to real-time environmental changes. This series of steps ensures comprehensive monitoring and analysis of the lighting environment, laying a solid foundation for the optimal adjustment of automated light sources.

[0037] Step S2: Obtain the historical operation report of the automated light source, mine the light source parameter preferences, and perform adaptive light source adjustment based on the real-time light environment perception map, thereby constructing an initial light source regulation strategy;

[0038] In this example, historical operation reports of the automated light source are obtained for light source parameter preference mining. Collect and organize the operation data of the light source to provide a basis for subsequent analysis and control strategies. Set the experimental parameters, such as setting the time range of historical data to the past 30 days to ensure the comprehensiveness of the data. When implementing the acquisition of historical data, first need to extract the historical operation report from the light source control system or database. These reports should include the running time of the light source, light intensity, energy consumption, adjustment frequency and environmental conditions, etc. The data can be extracted from the database using SQL query language to ensure that all relevant fields are obtained. In the specific implementation, a query script is written to retrieve the light source operation data in the past 30 days. The obtained data is stored in a data frame and processed using the Pandas library in Python. For example, pd.read_sql_query() can be used to convert the query results into a data frame format for subsequent analysis. During the acquisition of historical data, ensure the integrity and accuracy of the data, and handle data missing or outliers. The final generated historical operation report will provide necessary information support for subsequent light source parameter preference mining. After successfully obtaining the historical operation report of the light source, the light source parameter preference mining is performed. Analyze the historical data to identify the best operating parameters of the light source under different environmental conditions, thereby providing the basis for adaptive control strategies. Set the experimental parameters, such as setting the confidence level of the mining to 95% to ensure the reliability of the results. In the implementation of parameter mining, first clean and preprocess the historical data to ensure the consistency of the data format and remove outliers. Then, use clustering analysis (such as K-means clustering) to identify the running mode of the light source under different light conditions. The light intensity, running time and energy consumption can be used as clustering features. In the specific implementation, the K-means algorithm in the scikit-learn library is used to cluster the data. Set the number of clusters (for example, set to 3 clusters: low, medium and high light demand), and train the model through the KMeans class. Analyze the center point of each cluster to determine the corresponding best light source parameters. Combine the environmental conditions and light source parameters of each cluster to generate the preferred parameter set of the light source. For example, in the case of high light demand, the brightness of the light source may need to be increased or the running time may need to be extended. The final generated light source parameter preference will provide necessary information support for subsequent adaptive light source adjustment. After completing the light source parameter preference mining, the adaptive light source adjustment is performed according to the real-time light environment perception map. According to the current environmental light conditions, the running parameters of the light source are intelligently adjusted to optimize the lighting effect. Set the experimental parameters, such as setting the response time of the light source adjustment to 2 seconds to ensure fast adaptation to environmental changes. In the implementation of adaptive adjustment, first obtain the light intensity data in the real-time light environment perception map. Compare the current environmental light intensity with the historical preference parameters to determine whether the output of the light source needs to be adjusted.In specific implementation, conditional judgment statements can be used to determine the adjustment direction of the light source according to the difference between the real-time light intensity and the preference parameter. For example, if the current light intensity is lower than the lower limit of the preference parameter, the brightness of the light source can be increased; otherwise, the brightness of the light source can be reduced. Then, the control module (such as Arduino or PLC) sends adjustment instructions to the light source. Ensure that the control instructions can be transmitted in real time, and adjust the brightness and running time of the light source in time to adapt to the current lighting demand. The generated adaptive light source adjustment strategy will provide the basis for the intelligent adjustment of the light source, forming the initial light source control strategy. Through this series of steps, the intelligent management of the running state of the light source is ensured, and a solid foundation is laid for the automatic adjustment of the light source based on real-time environmental changes.

[0039] Step S3: Perform light source control according to the initial light source control strategy, and perform spatial light source disturbance detection to generate multi-dimensional features of the disturbed light source;

[0040] In this embodiment, light source control is executed according to the initial light source regulation strategy. The light source is intelligently adjusted according to the previously constructed regulation strategy to achieve the best lighting effect. Set the experimental parameters, such as setting the response time of light source adjustment to 2 seconds to ensure rapid adaptation to environmental changes. When implementing light source control, first extract relevant parameters from the initial light source regulation strategy, such as light source brightness, color temperature, and running time, etc. Through the control module (such as Arduino or PLC), these parameters are converted into control signals and sent to the light source device. In specific implementation, use Python or C++ to write control programs to ensure that sensor data can be received in real time and control instructions can be generated according to the regulation strategy. For example, if the current light intensity is lower than the set value, the brightness of the light source can be adjusted through PWM (Pulse Width Modulation) technology. During the control process, the actual output state of the light source needs to be monitored to ensure that it is consistent with the expected value. A feedback loop can be used to transmit the actual light intensity of the light source back to the control system and compare it with the preset value. If a deviation is found, the control strategy needs to be adjusted to achieve more accurate light source adjustment. Through this series of control execution, the light source can intelligently adjust according to real-time environmental changes to achieve the desired lighting effect, providing a stable foundation for subsequent disturbance detection. After successfully executing light source control, spatial light source disturbance detection is performed. Monitor whether there are uneven lighting, flickering or other disturbance phenomena during the operation of the light source to ensure the stability and reliability of the light source. Set the experimental parameters, such as setting the sampling frequency of disturbance detection to 5 times per second to capture the changes in the light source in a timely manner. When implementing disturbance detection, first deploy light sensors (such as photodiodes or photometers) in the light source irradiation area to monitor the changes in light intensity in real time. The sensors should be distributed in different positions to obtain comprehensive data on the spatial light distribution. In specific implementation, use a data acquisition system (such as Arduino or Raspberry Pi) to regularly read sensor data. Each sensor will record the light intensity and store the data in an array or data frame. By setting a threshold, determine whether the change in light intensity exceeds the normal range. For example, if the light intensity at a certain location fluctuates more than the set ±10% threshold, it can be considered that there is a disturbance in the light source. During the detection process, record the time and location of the disturbance occurrence in a timely manner for subsequent analysis. Use visualization tools (such as Matplotlib) to plot the light intensity versus time chart to visually display the stability of the light source and possible disturbance conditions. The final disturbance detection data will provide a basis for subsequent multi-dimensional feature analysis to ensure that the working state of the light source can be evaluated comprehensively. After completing the spatial light source disturbance detection, generate the multi-dimensional features of the disturbed light source. By analyzing the disturbance data, extract various features that affect the performance of the light source to provide a basis for subsequent adjustment strategy optimization. Set the experimental parameters, such as setting the time window for feature extraction to 60 seconds to ensure data stability.When implementing feature generation, first extract key indicators from previous disturbance detection data. These indicators may include the average, standard deviation, maximum, minimum, fluctuation amplitude, etc. of the light intensity. These statistical features can be calculated using the numpy library in Python. In specific implementation, traverse the data recorded by each sensor and calculate the statistical values of the light intensity. For example, calculate the light intensity fluctuation of each sensor within 60 seconds to evaluate its stability. By defining appropriate feature sets, ensure that the performance of the light source under different conditions can be reflected. Integrate the extracted multi-dimensional features into a data structure (such as a data frame or JSON format) for subsequent analysis and visualization. The output of each feature should include the feature name, value and corresponding sensor location. The final generated multi-dimensional features of the disturbed light source will provide important data support for the optimization of light source adjustment strategy, ensuring that the disturbance phenomenon can be reduced as much as possible in subsequent light source regulation. Through this series of steps, comprehensive monitoring and analysis of light source performance is ensured, laying a solid foundation for automatic light source adjustment based on real-time environmental changes.

[0041] Step S4: Perform light source change trend evolution analysis on the multi-dimensional features of the disturbed light source, and perform intelligent light disturbance reverse compensation to construct an intelligent anti-interference light source regulation strategy.

[0042] In this embodiment, the multi-dimensional features of the disturbed light source are analyzed for light source change trend evolution. By analyzing the historical disturbance features, the change pattern of the light source and the potential influencing factors are identified to provide a basis for subsequent reverse compensation. Set the experimental parameters, for example, set the analysis time window to 60 seconds to ensure the stability and representativeness of the data. When implementing the change trend analysis, first extract the key indicators from the previously generated multi-dimensional feature data, such as average illumination intensity, fluctuation amplitude, standard deviation, etc. These indicators will be used to evaluate the stability of the light source and the intensity of the disturbance. In the implementation, the Pandas library of Python can be used for data processing. By calculating the changes of the illumination intensity in different time periods, time series data is generated. Time series graphs can be drawn to show the trend of the illumination intensity over time, and the change trend of the light source can be analyzed intuitively. Combined with statistical analysis methods such as time series analysis or sliding window analysis, the significant change points of the illumination intensity and their corresponding environmental conditions are identified. Through these analyses, the main factors affecting the stability of the light source are determined, such as external light source interference, light source aging, etc. The final change trend analysis results will provide necessary information support for subsequent intelligent light disturbance reverse compensation, ensuring that the root cause and impact of the disturbance can be accurately identified. After completing the light source change trend analysis, intelligent light disturbance reverse compensation is performed. By adjusting the parameters of the light source in real time, the illumination changes caused by disturbances are compensated for, ensuring the stability and reliability of the light source. Set the experimental parameters, for example, set the compensation response time to 1 second to ensure real-time performance. When implementing reverse compensation, first identify the deviation of the current light source based on the results of the change trend analysis. For example, if the detected illumination intensity is lower than the set ideal value, the required light source output adjustment amount for compensation needs to be calculated. In the implementation, the PID control algorithm can be used to realize the compensation adjustment of the light source. The PID controller will calculate the adjustment amount based on the error between the current illumination intensity and the target intensity. Set appropriate PID parameters (such as proportional, integral, and derivative coefficients) to ensure the accuracy and stability of the compensation. Through the control module (such as Arduino or PLC), the calculated adjustment instructions are sent to the light source device in real time. Ensure that the light source can respond quickly according to the feedback to achieve real-time illumination compensation. This process will ensure that the light source can maintain stable illumination output under disturbance conditions, improving the adaptability and anti-interference ability of the light source. After completing the intelligent light disturbance reverse compensation, the intelligent anti-interference light source regulation strategy is constructed. The results of the previous analysis and compensation are integrated to form a systematic light source regulation strategy to adapt to different environmental changes. Set the experimental parameters, for example, set the update frequency of the regulation strategy to every 30 seconds to ensure the timeliness of the strategy. When implementing the regulation strategy construction, first integrate the results of the change trend analysis and reverse compensation to determine the optimal light source settings under different illumination conditions. According to historical data and real-time monitoring results, set the light source adjustment scheme under different environments.In specific implementation, a set of rules or algorithms is formulated to realize intelligent control of the light source through an embedded system or a cloud platform. For example, when the light intensity is below a certain threshold, the light source automatically increases the brightness; when the light intensity is above a certain threshold, the light source automatically reduces the brightness. In addition, a machine learning model can be introduced to train the model through historical data, so that it can predict the future trend of light changes. Based on these prediction results, the light source parameters are intelligently adjusted to achieve more efficient light regulation.

[0043] Step S5: Dynamic light source parameter regulation and expected response speed delay calculation based on the intelligent anti-interference light source regulation strategy to obtain the light source response time delay value;

[0044] In this embodiment, the current light source adjustment target parameters such as illumination intensity, color temperature, and running time are extracted from the intelligent anti-interference light source regulation strategy. These parameters should be updated based on real-time environmental lighting data and historical operation reports. In specific implementation, the control system (such as Arduino or PLC) is used to convert the regulation strategy into actual control instructions. PWM (Pulse Width Modulation) technology can be used to adjust the brightness of the light source, ensuring that it can be adjusted in detail according to the current lighting needs. During the control process, real-time monitoring of environmental lighting changes is carried out through sensors to continue reading lighting data, ensuring that the regulation can respond to environmental changes in a timely manner. If the environmental lighting suddenly decreases, the system should quickly increase the brightness of the light source; conversely, if the environmental lighting improves, the brightness of the light source needs to be reduced. Through this dynamic regulation, the light source can maintain stable lighting effects under various environmental conditions, improving user experience and safety. After successful dynamic light source parameter regulation, the expected response speed delay calculation is performed. The time required for the light source to reach the target illumination intensity after executing the adjustment instruction is quantified. Set the experimental parameters, such as setting the time window for delay calculation to 10 seconds to capture changes during the adjustment process. In the implementation of delay calculation, the baseline illumination intensity and target illumination intensity need to be set first. Through real-time monitoring of illumination intensity by the illumination sensor, the timestamp of the light source issuing the adjustment instruction and the timestamp of actually reaching the target intensity are recorded. In specific implementation, a control program can be written to record the time of each adjustment and the corresponding change in illumination intensity. Data collection is performed using Python or C++, ensuring that sensor feedback data can be accurately obtained. For example, after each illumination adjustment, the current time is recorded using the time.time() function, and compared with the time when the target intensity is reached. By analyzing these data, the response time delay of the light source can be calculated. The specific calculation formula is: response time delay = target reaching time - adjustment instruction issuing time; In multiple adjustment processes, record the delay value of each time and calculate its average value and standard deviation to evaluate the response performance of the light source. After completing the response time delay calculation, the results are analyzed to optimize the light source regulation strategy. By analyzing the delay data, the key factors affecting the response speed of the light source are identified to optimize accordingly. Set the experimental parameters, such as setting the target delay for optimization to be less than 1 second to ensure the real-time performance of the system. Summarize the recorded response time delay data and draw a delay time distribution chart to visually display the response performance of the light source. Matplotlib library can be used to generate graphics to show the delay under different adjustment conditions. Analyze the factors affecting the response time delay of the light source, such as the transmission delay of the control signal, the response speed of the light source device, and the feedback time of the sensor. By comparing the delay data under different conditions, the main bottlenecks are identified. According to the analysis results, the regulation strategy is optimized. For example, if it is found that there is a delay in the transmission of control signals, a faster communication protocol or a reduction in the complexity of signal transmission can be considered.At the same time, check the performance of the light source device to ensure that it can respond quickly after receiving instructions.

[0045] Step S6: Based on the light source response time delay value, adaptive error repair decisions are made, and dynamic feedback reinforcement learning is performed to build an intelligent light source feedback adjustment model.

[0046] In this embodiment, adaptive error repair decisions are made based on the light source response time delay value. By analyzing the delay value, errors generated during light source adjustment are identified, and appropriate repair strategies are developed. Set the experimental parameters, such as setting the error repair threshold to ±5% of the target light intensity, to ensure the accuracy of light source adjustment. When implementing error repair decisions, first obtain the actual light intensity data achieved from the previous response time delay calculation, and compare it with the target light intensity. By calculating the error value, determine whether the light source adjustment has achieved the expected effect. The error calculation formula is: error = target light intensity - actual light intensity; use Python to write a program to periodically check the light intensity through a timing task. If the error exceeds the set threshold, decide whether to perform error repair. For example, if the actual light intensity is lower than the target value, the system should immediately issue a signal to increase the light source brightness. In the decision-making process, combine historical data and current environmental light conditions to judge the rationality of light source adjustment. Use conditional statements to ensure that appropriate repair strategies are developed under different environmental conditions. Through this series of adaptive error repair decisions, ensure that the light source can quickly respond to errors during adjustment and maintain stable light output. After successfully implementing adaptive error repair decisions, perform dynamic feedback reinforcement learning. Through machine learning techniques, continuously optimize light source adjustment strategies to adapt to real-time environmental changes and improve the intelligence level of the system. Set the experimental parameters, such as setting the learning rate to 0.01 to balance learning and stability. When implementing feedback reinforcement learning, first define the state space, action space, and reward mechanism. The state space can include the current light intensity, environmental light conditions, and current light source output, etc.; the action space is the possible light source adjustment operations (such as increasing brightness, decreasing brightness, etc.). In specific implementation, use Q-learning or deep Q network (DQN) algorithm to learn through interaction with the environment. Initialize the Q value table, randomly select the initial state, record the light intensity and error after each adjustment, and adjust the Q value according to the feedback. In each iteration, use the ε-greedy strategy to select actions, i.e. select random actions with a certain probability to explore new strategies; the rest of the time, select the action with the highest Q value. Through the combination of environmental feedback, update the Q value: ; where r is the reward of the current action, a is the learning rate, and g is the discount factor. Through continuous learning and feedback, the light source adjustment strategy is gradually optimized to achieve the best lighting effect under different environmental conditions. After completing dynamic feedback reinforcement learning, an intelligent light source feedback adjustment model is constructed. The learned adjustment strategy is integrated into a complete model to facilitate real-time light source adjustment in practical applications. Set the experimental parameters, such as setting the model update frequency to every 30 seconds to ensure the timeliness of the strategy. When implementing model construction, first save the learned Q-value table or policy function as model parameters. Model serialization can be performed using Python's pickle library to ensure quick loading in subsequent applications. Build an encapsulation class that provides an interface for real-time light source adjustment. This class should include methods for loading the model, receiving environmental inputs, and outputting adjustment instructions. By implementing these methods, ensure that the model can function in practical applications. During application, real-time monitoring of environmental lighting changes is performed, and the intelligent light source feedback adjustment model is called according to the current state to generate corresponding light source adjustment instructions. Ensure that the system can quickly respond to real-time data and optimize lighting effects.

[0047] In this embodiment, referring to Figure 2 For the detailed implementation steps of step S1, the detailed implementation steps of step S1 include:

[0048] According to the distributed light environment sensor array, the environmental lighting monitoring parameters of the automatic light source are continuously collected;

[0049] The environmental lighting monitoring parameters are tracked in real time to obtain the spatial light propagation trajectory;

[0050] The spatial light propagation trajectory is analyzed for light path distribution, and a light path distribution map is constructed;

[0051] The light intensity value of the environmental lighting monitoring parameters is calculated; the light intensity value is analyzed for time-domain change trend, and a light intensity trend change curve is constructed;

[0052] According to the light path distribution map, a dynamic light environment perception model is constructed for the light intensity trend change curve, and a real-time light environment perception map is constructed.

[0053] In this embodiment, a distributed light environment sensor array is deployed to continuously monitor the ambient lighting parameters of the automated light source. The sensor array should cover different locations of the target area, ensuring that it can capture the lighting changes of the light source and the surrounding environment. The selected sensors, such as photodiodes or photoresistors, should have high sensitivity and a wide measurement range to accurately collect data under different lighting conditions. In the implementation process, the data collection frequency is set to once per second, and multiple data checks are performed to filter out abnormal values. For example, a higher threshold (such as 1000 lx) is set to exclude strong light interference. At the same time, other parameters of the environment are recorded, such as temperature (e.g., 20°C to 30°C) and humidity (e.g., 40% to 70%), which may affect the propagation characteristics of light. By combining these data, a more comprehensive understanding of the changes in the light environment can be achieved. After obtaining the lighting monitoring parameters, real-time light path tracking is performed. Computer vision and ray tracing algorithms are used to simulate the propagation of light in space. A three-dimensional environmental model is established, which should include the position, shape of the light source, and geometric characteristics of surrounding objects. Using ray tracing technology, the light rays are simulated to be emitted from the light source, pass through air, glass, and other media, and interact with objects in the environment. Detailed information of each light path, including light intensity, incidence angle, and reflection direction, can be recorded. To improve tracking efficiency, hierarchical sampling and ray caching techniques are used to reduce computational load and improve real-time performance. By combining the tracking results with the collected environmental lighting monitoring parameters, a dynamic light path database is formed, which facilitates subsequent analysis. After obtaining the information of the light path, light path distribution analysis is performed. By statistically analyzing the tracked light paths, a light path distribution map is constructed to visually display the distribution of light in space. Statistical methods are used for cluster analysis of light paths to identify areas with high light concentration and areas with large changes in light intensity. Visualization tools such as histograms and heat maps are used to present the light path distribution information in graphical form, making it easier to understand the distribution characteristics of light in space. Machine learning algorithms such as K-means clustering are introduced to further optimize the analysis process of light path distribution.

[0054] Through learning from historical data, the model can quickly identify the distribution characteristics of light paths in new light environments. After analyzing the light path distribution, the ambient light monitoring parameter, light intensity value, is calculated. This process involves combining the collected light data with the light paths to achieve more accurate light intensity calculation. The contribution of different light paths to the light intensity is analyzed. By weighting the light intensity of each light path, the comprehensive ambient light intensity value is obtained. The weighting factor can be adjusted according to the length and reflection angle of the path to more realistically reflect the light intensity. After calculation, a time series dataset is obtained, recording the light intensity values at different time points. This dataset provides the basis for subsequent time-domain trend analysis. The light intensity values are analyzed for time-domain trend analysis to construct a light intensity trend curve. Through analysis of the time series data, the change pattern of light intensity is identified, including peaks and troughs. In this process, smoothing algorithms such as moving average or exponential weighted moving average are used to highlight the main trend by eliminating noise. At the same time, Fourier transform is used to analyze periodic changes, and periodic characteristics of light changes are identified through frequency domain analysis. Finally, a light intensity trend curve is generated, showing the variation of light intensity over time. This curve not only reflects the fluctuations in light intensity, but also provides important evidence for the adjustment of automated light sources.

[0055] According to the light path distribution map and the light intensity trend curve, dynamic light environment perception modeling is performed to construct a real-time light environment perception map. This map reflects the current environmental lighting state in real time, providing decision support for the adjustment of automated light sources. In the modeling process, interpolation methods or neural network models are used to combine light path distribution and light intensity data. This model can be updated in real time, continuously adjusting the output of light sources as the environment changes to optimize lighting effects. Through the implementation of the above steps, real-time monitoring and analysis of light environment changes are achieved, and intelligent adjustment of automated light sources is realized, improving energy utilization efficiency and user experience.

[0056] In this embodiment, refer to Figure 3 For the detailed implementation step flowchart of step S2, in this embodiment, the detailed implementation steps of step S2 include:

[0057] Obtain the historical operation report of the automated light source; identify user light adjustment behaviors according to the historical operation report of the automated light source, and extract all user light adjustment behavior data;

[0058] Calculate the adjusted light intensity and color temperature of the user light adjustment behavior data to obtain light adjustment behavior characteristics;

[0059] Calculate the timestamp of the user light adjustment behavior data to obtain the time node of each light adjustment behavior;

[0060] According to the time node, light adjustment behavior characteristics are subjected to light source parameter preference mining to obtain an individualized light source parameter preference mode;

[0061] Based on the individualized light source parameter preference mode, light source adjustment priority analysis is performed, and priority sorting processing is performed to generate an individualized light source parameter configuration matrix;

[0062] According to the real-time light environment perception map, the individualized light source parameter configuration matrix is subjected to adaptive light source adjustment, so as to construct an initial light source regulation strategy.

[0063] In this example, historical operation reports of automated light sources are extracted from the system database. These reports typically contain information such as the running time of the light source, light intensity, color temperature, user adjustment records, and ambient lighting conditions. The format of the historical operation reports should be uniform to facilitate subsequent data processing. The extraction process can use SQL queries or data scraping techniques to obtain the required historical data. Set query conditions such as time period (e.g., the past month) and light source type (e.g., LED, fluorescent lamp) to ensure that the data obtained is representative. The amount of data extracted should be large enough for effective analysis, with a recommendation of at least 500 records. Preprocessing of the extracted data is required, including data cleaning and standardization. Remove missing values and outliers to ensure data quality. Standardization can be used to unify the range of light intensity (unit: lx) and color temperature (unit: K) to facilitate subsequent comparison and analysis. After obtaining the historical operation reports, identify user light adjustment behavior. By analyzing historical data, extract user light adjustment behavior data at different time periods. These behaviors may include adjustments to light intensity (e.g., from 300 lx to 600 lx) and changes in color temperature (e.g., from 2700 K to 5000 K). To achieve this goal, event detection algorithms can be applied to identify the time points and adjustment amplitudes of user adjustment operations. Set thresholds, such as a change in light intensity of more than 50 lx or a change in color temperature of more than 200 K, as valid adjustment behavior. Record the specific parameters of each adjustment behavior, including the light intensity before adjustment, the light intensity after adjustment, the color temperature before adjustment, and the color temperature after adjustment. Through these behavior data, construct a user adjustment behavior feature set and extract each user's adjustment preferences, such as whether they prefer higher color temperature or lower light intensity. Analyze the frequency of user adjustment behavior to identify common adjustment patterns. After extracting user light adjustment behavior data, calculate the adjusted light intensity and color temperature to obtain light adjustment behavior features. This process combines light adjustment behavior data with user's basic characteristics, such as age, gender, etc., to better understand adjustment preferences. Calculate the light intensity change and color temperature change of each adjustment behavior. Establish a data table to record the specific parameters of each adjustment, such as the light intensity before and after adjustment (e.g., 300 lx to 600 lx) and the color temperature (e.g., 2700 K to 5000 K). Then, calculate the average change of all adjustment behaviors to obtain overall adjustment features. In addition, use clustering analysis methods (such as K-means) to group user adjustment behaviors and identify commonalities among different user groups. For example, one group may prefer high light intensity and high color temperature, while another group prefers low light intensity and warm color. Through these feature data, we can better understand user's light adjustment preferences. After calculating the light adjustment behavior features, analyze the time stamp of user adjustment behavior to obtain the time node of each light adjustment behavior. The time stamp records the specific time of each adjustment behavior, such as "2023-05-15 18:30:00".The key of this stage is to combine the timestamp data with the light adjustment behavior data. A comprehensive data table containing timestamp, light intensity, color temperature, and adjustment type can be created. By analyzing the adjustment behavior at different time periods, the trend of users adjusting more frequently at certain times (e.g., from 6 pm to 9 pm) is identified. Additionally, time series analysis is conducted to calculate the interval time of adjustment behavior, understanding the user's adjustment habits. For example, a user may adjust the light at 8 pm every night. Through these analyses, the user's daily behavior patterns can be understood, providing a basis for subsequent personalized light source parameter preference mining. According to the time node, light adjustment behavior characteristics are mined for light source parameter preferences. This process aims to identify users' light adjustment preferences at different time periods and build personalized light source parameter preference patterns. Using association rule mining algorithms (such as the Apriori algorithm), the adjustment behavior of users at different time nodes is analyzed, and the correlation between light intensity and color temperature is identified. For example, at 8 pm, users may prefer to adjust the light intensity to 300 lx and the color temperature to 3000 K. Record these preference patterns to generate personalized configurations. At the same time, combining the frequency and time node of the user's light adjustment behavior, the user's light source parameter preferences at different time periods are calculated. For example, analysis shows that a user prefers high light intensity (e.g., 600 lx) during work hours (e.g., during the day) and low light intensity (e.g., 300 lx) and warm color (e.g., 2700 K) during rest time (e.g., at night). After completing the construction of personalized light source parameter preference patterns, light source adjustment priority analysis is conducted based on the patterns, and priority sorting is processed to generate a personalized light source parameter configuration matrix. This matrix will list in detail the light source adjustment priorities of each user at a specific time period. By calculating the preferred light intensity and color temperature of each user, a priority scoring system is constructed. For example, for each time period, different weights are given according to the user's adjustment frequency and preference degree. Higher frequency and strong preference will give higher priority scores. The generated personalized light source parameter configuration matrix will include user identification, time period, recommended light intensity and color temperature, and priority score. This matrix provides a basis for subsequent adaptive light source adjustment. According to the real-time light environment perception map, adaptive light source adjustment is conducted based on the personalized light source parameter configuration matrix. The real-time light environment perception map provides the current state of the environment light, facilitating the system to dynamically adjust according to the user's personalized preferences. Combined with the user's light source parameter configuration matrix and environmental lighting data, adaptive adjustment strategies are implemented. For example, when the environmental light intensity is lower than a certain threshold (e.g., 200 lx), the light source is automatically adjusted to the user's preferred light intensity (e.g., 600 lx). At the same time, the color temperature is adjusted to match the user's preference. Through these steps, an initial light source control strategy is constructed, ensuring that the light source can be optimized and adjusted according to the user's personalized needs and real-time environmental changes, improving user experience and energy utilization efficiency.

[0064] In this embodiment, the specific steps for constructing the initial light source regulation strategy based on the real-time light environment perception map and adaptive light source adjustment of the personalized light source parameter configuration matrix are as follows:

[0065] Identify the user's current activity state based on the user's light adjustment behavior data.

[0066] Perform light source brightness demand analysis based on the user's current activity state to obtain the current user light source brightness demand.

[0067] Perform environment light source brightness demand deviation calculation on the real-time light environment perception map based on the current user light source brightness demand to obtain the light brightness demand value of the current environment.

[0068] Perform adaptive light source adjustment on the personalized light source parameter configuration matrix based on the light brightness demand value to construct the initial light source regulation strategy.

[0069] In this embodiment, the user's current activity state is identified based on the user's light adjustment behavior data. This process involves analyzing previously collected adjustment behaviors to infer the user's activity type, such as work, leisure, entertainment, or dining, etc. Machine learning classification algorithms (such as decision trees or support vector machines) are used to model the user's adjustment behavior. Input features include the user's adjusted light intensity, color temperature, timestamp, and historical adjustment records. For example, if the user adjusts the light intensity to 600 lx and the color temperature to 4000 K at 9 am, it can be inferred that they are working. Secondly, set the threshold for activity state. For example, if the user frequently adjusts to high brightness and neutral color temperature within a certain time period (such as 9:00-12:00), their activity state will be marked as "work". Conversely, if the user adjusts the light source to 300 lx and 3000 K at 8 pm, it can be inferred that they are resting or entertaining. Through the identification of these behavior patterns, a foundation is provided for subsequent light source brightness demand analysis. After identifying the user's current activity state, light source brightness demand analysis is performed. Based on the user's activity state, the required light source brightness is inferred. For example, for the "work" state, the user may require higher light intensity (such as 500 lx to 800 lx), while in the "leisure" state, they may prefer lower light intensity (such as 200 lx to 400 lx).

[0070] In the process of implementation, a light source brightness demand model is established to associate different activity states with corresponding light intensity values. The average light intensity demand under each activity state can be calculated through regression analysis of user adjustment behavior data. Suppose historical data shows that the average light intensity of the user in the working state is 650 lx, and the average light intensity in the leisure state is 300 lx, these data will be used as the basis for light source brightness demand analysis. In addition, clustering analysis method is used to further subdivide the user's brightness demand. For example, the user is divided into three categories: high demand, medium demand and low demand, and according to the different activity states, the corresponding light source brightness standards are formulated. This analysis provides an important basis for subsequent calculation of the deviation of the environmental light source brightness demand of the real-time light environment perception map. After determining the user's light source brightness demand, the deviation of the environmental light source brightness demand of the real-time light environment perception map is calculated. This process aims to evaluate the gap between the current environmental lighting conditions and the user's required light source brightness. The current environmental lighting intensity value (such as 200 lx) is obtained from the real-time light environment perception map. Compare this value with the user's current light source brightness demand. For example, if the user's light source brightness demand is 500 lx, and the real-time environmental lighting is 200 lx, the deviation is 300 lx. In order to calculate the deviation, a formula is set: deviation = user light source brightness demand - current environmental lighting intensity; In the calculation process, the volatility of environmental lighting should be considered. For example, if the environmental lighting changes at different time periods, the deviation should be dynamically adjusted. By setting a threshold value (such as ± 50 lx), the accuracy of the deviation calculation is ensured based on real-time monitoring.

[0071] According to the light brightness demand value, the personalized light source parameter configuration matrix is adjusted adaptively. This step aims to automatically adjust the light source parameters according to the user's light source brightness demand and the current environmental lighting conditions, in order to improve the user experience. Analyze the personalized light source parameter configuration matrix to determine the user's light source settings under a specific activity state. For example, for a user whose current activity state is "working", it is recommended to adjust the light source to a brightness of 650 lx and a color temperature of 4000 K. Combined with the deviation calculation result, if the current environmental lighting is 200 lx, the light source needs to be increased to 850 lx. Set the adaptive adjustment strategy and use the feedback control system to adjust the light source. Real-time monitoring of light intensity changes is achieved through sensors, and dynamic adjustment of light source output is achieved. For example, if the light source brightness is set to 850 lx, the system will continuously monitor and ensure that the lighting intensity remains at this value, and if the environmental lighting increases (such as reaching 300 lx), the light source brightness can be adjusted accordingly. Record the data of each light source adjustment for subsequent analysis and optimization. This process will ensure that the light source can be adjusted appropriately according to the user's individual needs and real-time environmental changes, and will build an initial light source control strategy to improve the overall lighting effect and user satisfaction.

[0072] In this embodiment, reference is made toFigure 4 For the detailed implementation step flowchart of step S3, in this embodiment, the detailed implementation step of step S3 includes:

[0073] According to the initial light source regulation strategy, light source control is executed, and full-space light intensity monitoring is performed to obtain a full-range light monitoring graph;

[0074] The full-range light monitoring graph is subjected to multi-scale wavelet decomposition to extract light frequency domain graphs of different scales;

[0075] The light frequency domain graphs are subjected to high-frequency interference detection on a scale-by-scale basis to identify light source interference points in the space;

[0076] Abnormal light source fluctuation analysis is performed on the light source interference points in the space to obtain abnormal light source factors in the space;

[0077] The real-time fluctuation frequency, fluctuation intensity and fluctuation direction of the abnormal light source factors in the space are calculated to obtain multi-dimensional characteristics of the disturbance light source.

[0078] In this embodiment, after the initial light source regulation strategy is established, the light source control execution is performed. This process involves adjusting the light source according to the preset parameters to ensure that the ambient light meets the user's needs. Using the intelligent lighting control system, the light source brightness and color temperature are adjusted to the set values through wireless network or wired connection. For example, the light source brightness is adjusted to 650 lx, and the color temperature is adjusted to 4000 K. Then the full-space light intensity monitoring system is started, and a distributed light sensor array is used to monitor the space comprehensively. Each sensor records the ambient light intensity, and the data acquisition frequency is set to once per second. Ensure that the sensor covers every corner of the space to obtain comprehensive light data. At this time, the sensor should have high sensitivity and be able to effectively capture changes under different light conditions. The collected light data will be integrated and a full-range light monitoring map will be generated, which shows the light intensity distribution of each location in the space. This monitoring map can be used for subsequent analysis to identify light uniformity and potential interference points. After obtaining the full-range light monitoring map, multi-scale wavelet decomposition is performed to extract light frequency domain graphs of different scales. Wavelet transform is an effective signal processing method that can analyze signals in both time and frequency domains, suitable for light data analysis. Choose an appropriate wavelet basis (such as Daubechies wavelet or Haar wavelet) for wavelet transform. Set the decomposition level, for example, perform three-layer decomposition to obtain light information of different frequencies. Each layer of wavelet decomposition will provide light frequency domain graphs of different scales, corresponding to high-frequency components and low-frequency components respectively. Through wavelet coefficient extraction, the light frequency domain graph of each layer will be stored for subsequent analysis. For example, the first layer may reflect large-scale light changes, the second layer captures medium-frequency changes, and the third layer focuses on subtle light fluctuations. These frequency domain graphs will provide a basis for subsequent high-frequency interference detection and anomaly analysis. After obtaining light frequency domain graphs of different scales, high-frequency interference detection is performed at each scale. High-frequency components usually represent noise and interference in the light signal, so identifying these components is crucial for understanding the stability of the light source. In the specific implementation process, set the threshold value of high-frequency interference. For example, the standard deviation of high-frequency components exceeding a certain value (such as 0.1 lx) is considered as interference signal. Use signal processing algorithms (such as Fast Fourier Transform) to analyze each frequency domain graph and identify abnormal light fluctuation points. By statistically analyzing the high-frequency components of each scale, record the number and location of interference points. For example, 10 interference points are found in a certain area, and their light fluctuations exceed the threshold value. This information will be used for subsequent analysis of abnormal light source fluctuations.

[0079] After identifying the light source disturbance points within the space, an abnormal light source fluctuation analysis is conducted to obtain the spatial abnormal light source factors. This step aims to deeply analyze the characteristics of the disturbance points to identify their impact on the overall lighting environment. The lighting data of all disturbance points is aggregated, and their fluctuation patterns are analyzed. Statistical analysis methods such as mean and variance analysis are used to understand the lighting variation trends of the disturbance points. For example, the average lighting intensity and fluctuation amplitude of the disturbance points during the monitoring period are calculated. If the light intensity of a certain disturbance point increases sharply within a short period of time, it may indicate a light source failure or external light source influence. A light source fluctuation model is constructed to compare the fluctuation characteristics of the disturbance points with the overall lighting environment, identifying abnormal light source factors. Visualization tools such as heat maps or scatter plots are used to display the distribution and intensity of the disturbance points, facilitating intuitive understanding of their impact range. Finally, the real-time fluctuation frequency, fluctuation intensity, and fluctuation direction of the spatial abnormal light source factors are calculated to obtain the multi-dimensional characteristics of the disturbance light sources. This step aims to comprehensively describe the disturbance characteristics of the light sources, providing data support for subsequent adjustment strategies. The calculation of real-time fluctuation frequency can be achieved by observing the number of disturbance events within a certain time window. For example, if 5 disturbances are detected within 5 minutes, the fluctuation frequency is 1 time / minute. A frequency threshold (such as 0.5 times / minute) is set to identify frequently fluctuating disturbance sources. Secondly, the fluctuation intensity, i.e., the amplitude of the lighting change of the disturbance points, is calculated. By comparing the lighting intensity before and after the disturbance, the fluctuation intensity value (such as a maximum intensity change of 50 lx) is obtained. This data helps to understand the severity of the disturbance. Finally, the fluctuation direction is identified. By analyzing the trend of the lighting change of the disturbance points, it is determined whether it is increasing or decreasing. For example, if the lighting intensity increases from 200 lx to 300 lx, the fluctuation direction is "increase". These multi-dimensional characteristics are aggregated to form a complete description of the disturbance light source characteristics, providing a basis for subsequent light source adjustment and optimization strategies. Through the implementation of the above steps, comprehensive monitoring and analysis of spatial light source disturbances are achieved, providing reliable data support and decision-making basis for automated light source adjustment.

[0080] In this embodiment, step S4 includes the following steps:

[0081] Perform light source change trend evolution analysis on the multi-dimensional characteristics of the disturbance light sources, and extract the disturbance light source trend evolution characteristics.

[0082] Perform disturbance trajectory development prediction on the disturbance light source trend evolution characteristics, and construct a disturbance light source trajectory evolution model.

[0083] Based on the disturbance light source trajectory evolution model, perform dynamic light source mapping on the real-time light environment perception map to obtain a light environment disturbance light source mapping map.

[0084] Based on the light environment disturbance light source mapping map, perform intelligent light disturbance reverse compensation on the initial light source regulation strategy to construct an intelligent anti-interference light source regulation strategy.

[0085] In this embodiment, the time series analysis method is used to analyze the multi-dimensional characteristics of the disturbed light source, such as illumination intensity, frequency, intensity, and direction. A time window (e.g., 5 minutes or 10 minutes) is set, and the illumination data within each time window is statistically analyzed to calculate the mean, variance, and maximum of the illumination intensity. Through these statistical indicators, the change trend of the light source can be identified. Secondly, the original data is smoothed using the moving average method to eliminate the influence of short-term fluctuations and highlight long-term trends. For example, a 3-minute sliding window is used to calculate the average value of the illumination intensity, obtaining a more stable light source change trend. Then, the analysis results are visualized, such as drawing the curve of illumination intensity over time, to facilitate intuitive understanding of the evolution characteristics of the disturbed light source. Through the above analysis, the evolution pattern of the disturbed light source can be identified, such as peak period, trough period, and their duration. These features will lay the foundation for subsequent trajectory development prediction. After extracting the evolution characteristics of the disturbed light source, the trajectory development prediction of the disturbed light source is carried out, and the trajectory evolution model of the disturbed light source is constructed. The goal of this step is to predict future light source changes based on historical data.

[0086] Select an appropriate prediction model, such as ARIMA (Autoregressive Integrated Moving Average) or LSTM (Long Short-Term Memory). Train the model using historical data (e.g., light intensity changes, frequency, intensity, etc.) to identify patterns in light source variations. For example, use the past 30 minutes of light intensity data to train an LSTM model to capture long-term dependencies in light changes. During training, set a training set and test set (e.g., 70% training, 30% testing) and evaluate the model's accuracy through cross-validation. After training, use the model to predict future light source changes and generate a trend graph of future light intensity changes. Compare the prediction results with historical data to evaluate the model's effectiveness. For example, calculate the root mean square error (RMSE) to assess the accuracy of the prediction. If the model performs well, use it to further analyze and adjust future light source changes. Based on the perturbed light source trajectory evolution model, perform dynamic light source mapping on the real-time light environment perception map to obtain a light environment perturbed light source mapping map. This process aims to combine predicted light source changes with actual environmental lighting conditions. Obtain the current light distribution data from the real-time light environment perception map and combine it with the prediction results of the perturbed light source trajectory evolution model. During dynamic light source mapping, identify which areas will be affected by light source disturbances and mark these areas on the light environment mapping map. Perform spatial interpolation (e.g., Kriging interpolation or inverse distance weighting) to generate a light environment perturbed light source mapping map. Based on the predicted changes in light sources and real-time monitoring data, calculate the deviation of light intensity at each location from the expected value to form a heat map representing the intensity of light interference. Use color coding in the mapping map to represent different levels of light interference. For example, red areas represent a large deviation in light intensity (e.g., >50 lx), while green areas represent stable light. This mapping map will provide important information for subsequent intelligent light disturbance reverse compensation. Based on the light environment perturbed light source mapping map, perform intelligent light disturbance reverse compensation on the initial light source control strategy to build an intelligent anti-interference light source control strategy. The goal of this step is to dynamically adjust light source settings based on light interference to improve light quality. Analyze the light environment perturbed light source mapping map to identify areas affected by interference and light deviation. For example, if the light intensity deviation in a certain area is -30 lx, indicating insufficient light, increase the light source brightness to compensate for the deficiency. Based on the information in the mapping map, develop appropriate light source adjustment strategies. For example, increase the light source brightness in the disturbed area to the user's desired target value (e.g., adjust to 600 lx). At the same time, consider adjusting the color temperature to ensure overall improvement in light quality. During implementation, use a feedback control system to continuously adjust light source settings based on real-time monitoring data. Monitor light intensity changes in real time by setting a threshold (e.g., ±10 lx) to ensure that the light source can respond to environmental changes in a timely manner. Finally, record the parameters and results of each adjustment to optimize the light source control strategy for future use.Through this intelligent anti-interference light source regulation strategy, not only the illumination quality can be improved, but also the interference brought by environmental changes can be effectively dealt with, and the lighting experience of users can be improved.

[0087] In this embodiment, step S5 includes the following steps:

[0088] Based on the intelligent anti-interference light source regulation strategy, dynamic light source parameter regulation is performed to calculate light source operation regulation parameters;

[0089] The light source operation regulation parameters are divided into multiple time points to obtain a light source parameter sequence at multiple time points;

[0090] Based on the light source parameter sequence, light source regulation response time is calculated to obtain a response time length of light source disturbance compensation regulation;

[0091] Based on the preset regulation response reference time, the response time length of light source disturbance compensation regulation is calculated to obtain a light source response time delay value.

[0092] In this embodiment, the current environmental illumination data is obtained from the real-time light environment perception system, for example, the current illumination intensity is 300 lx. Combined with the user's light source parameter demand (such as the target light intensity is 600 lx, and the color temperature is 4000 K), the regulation parameters required by the light source are calculated. The regulation formula is set as: regulation parameter = target light intensity - current environmental illumination intensity; in the calculation process, if the current environmental illumination intensity is 300 lx, the regulation parameter is 300 lx. By analyzing the trend of the current environment, the output of the light source is adjusted. For example, if the current brightness of the light source is 250 lx, it needs to be increased to 550 lx. The control algorithm (such as PID control) is used to accurately regulate the light source. The PID control will dynamically adjust the output power of the light source according to the deviation between the current illumination value and the target value, to ensure that the target illumination intensity is quickly and smoothly reached. This process will monitor the illumination change in real time, so as to make timely adjustment. After completing the dynamic light source parameter regulation, the light source operation regulation parameters are divided into multiple time points to obtain a light source parameter sequence at multiple time points. This process aims to analyze the change of light source parameters over time. Set the time interval (such as every 5 minutes or every 10 minutes) to record the data. Within this time frame, the system will record the regulation parameters of the light source regularly, including light intensity, color temperature and regulation amplitude. For example, within an hour, 12 time points of light source parameter data are recorded. Through data collection, a light source parameter sequence is constructed.

[0093] Assuming the light intensity at the first time point is 250 lx, the second time point is 300 lx, and so on. Organize these data into a sequence for subsequent analysis. This sequence will be used to evaluate the effects of light source regulation and response time. Plot the light source parameter changes to visualize the trend of light source parameters over time. Through chart analysis, the regulation mode of the light source and any potential abnormal fluctuations can be identified, which will provide the basis for subsequent response time calculation. After obtaining the sequence of light source parameters, the calculation of the response time of the light source regulation is carried out. This process aims to evaluate the time required for the light source to adjust in order to understand the efficiency of the regulation. The response time of the light source regulation is defined as the time required for the light source to reach the target light intensity from receiving the regulation instruction. By analyzing the sequence of light source parameters, the change in light intensity at each regulation time point is identified. For example, if the light source reaches the target light intensity at the 5th minute after the instruction is given at the first time point, the response time is 5 minutes. To further accurately calculate the response time, the progressive comparison method is used. Gradually compare the change in illumination intensity at each time point, and record the exact time when the light source reaches the target value. Set a threshold (such as ±10 lx) to ensure the accuracy of the light source when it reaches the target value. For example, when the light source reaches 590 lx during the adjustment process, it is considered to have reached the target.

[0094] Through statistical analysis, the average response time of the light source over a period of time is calculated. If the response times under multiple regulation instructions are 5 minutes, 6 minutes, and 4 minutes, respectively, the average response time is 5 minutes. After completing the response time calculation, based on the pre-set regulation response reference time, the expected response speed delay calculation of the response length of the light source disturbance compensation regulation is carried out to obtain the light source response time delay value. Set a reference response time, for example, 3 minutes. By comparing the actual response time with the reference time, the delay of the response time is calculated. For example, if the actual average response time is 5 minutes, the response delay is: response time delay = actual response time - reference response time; in this example, the response time delay is 2 minutes. In order to more accurately evaluate the delay value, the response time of different time periods can be analyzed respectively to identify the performance of the system under different conditions. Set a threshold (such as ±1 minute) to evaluate the acceptable range of response delay. If the delay exceeds this range, the light source regulation strategy needs to be further optimized to improve the response speed. By analyzing the delay data, potential system bottlenecks can be identified and targeted improvements can be made.

[0095] In this embodiment, step S6 includes the following steps:

[0096] Based on the light source response time delay value, light source system error attribution analysis is carried out to obtain light source system error factors;

[0097] Based on the light source system error factors, adaptive error repair decisions are made to obtain an adaptive error repair strategy;

[0098] Perform light regulation quality assessment on the light source operation regulation parameters, and extract the light environment regulation quality assessment index;

[0099] Perform time series change analysis on the light environment regulation quality assessment index to obtain the light environment quality evolution characteristics;

[0100] Perform dynamic feedback reinforcement learning according to the light environment quality evolution characteristics and the self-adaptive error repair strategy to construct an intelligent light source feedback regulation model.

[0101] In this embodiment, the response time delay data of the light source is collected and compared with the target response time. The range of data analysis is set, for example, the regulation records of the past week are compared, and the time period with larger delay is identified. On this basis, statistical analysis is performed to calculate the average value and standard deviation of the delay to evaluate the universality and stability of the error. Causal analysis method (such as fishbone diagram) is used to further analyze the causes of the delay. Possible error factors include sensor response time, control algorithm calculation delay, physical limitations of light source hardware, etc. For example, if it is found that the response time of the sensor is slow, experimental test can be performed to record the actual time from the reception of the signal to the start of the feedback by the sensor, and compare it with the ideal state. Finally, all identified error factors are classified (such as hardware problems, software problems and environmental influences), and targeted measures are developed. Through attribution analysis, the main factors affecting the regulation of the light source can be determined, providing data support for subsequent self-adaptive error repair decisions.

[0102] After identifying the error factors of the light source system, adaptive error repair decisions are made based on these factors to form an adaptive error repair strategy. This strategy will ensure that the light source system can quickly adjust when facing errors. For each error factor, repair measures are developed. For example, if the sensor response time is slow, consider replacing it with a high-performance sensor or optimizing the sensor's installation position to improve response sensitivity. If the control algorithm has a high calculation delay, optimize the algorithm and use more efficient calculation methods (such as parallel computing) to reduce the delay. Second, design an adaptive feedback mechanism to enable the system to monitor the running state of the light source in real time and automatically adjust the light source parameters according to the current error situation. For example, when detecting that the response time of the light intensity is delayed beyond the set threshold (such as 2 minutes), the system will automatically increase the output power of the light source to compensate for the lack of light in advance. In the implementation process, the effect of the repair strategy needs to be evaluated regularly. By monitoring the adjusted light response time, the improvement effect of the system is evaluated. For example, if the delay time is reduced from 5 minutes to 3 minutes after repair, it indicates that the adaptive repair strategy is effective. At the same time, record all the data of the repair process for subsequent analysis and optimization. After implementing the adaptive error repair strategy, the light control quality of the light source running control parameters is evaluated, and the light environment control quality evaluation index is extracted. The goal of this step is to quantify the effectiveness and reliability of light source control. Set indicators for light control quality evaluation, such as light uniformity, response time accuracy, and user satisfaction. Relevant data can be collected through user surveys, sensor data analysis, and actual observation. For example, when evaluating light uniformity, the standard deviation of light intensity in space can be calculated. The smaller the standard deviation, the more uniform the light.

[0103] Secondly, the calculation formula of the light environment regulation quality evaluation index is established. For example, the comprehensive evaluation index is set as: quality evaluation index = a x uniformity + β x response time accuracy + γ x user satisfaction; wherein a, β and γ are weight coefficients, which are adjusted according to the actual situation. Through comprehensive calculation, a whole light regulation quality evaluation index is obtained to facilitate subsequent analysis. After obtaining the light environment regulation quality evaluation index, time series change analysis is carried out to obtain the light environment quality evolution characteristics. This step aims to identify the change law of light regulation quality over time. The light regulation quality evaluation index data in the past period (such as monthly data in the past three months) is collected and arranged into a time series. Time series analysis methods (such as autoregressive moving average model) can be used to model the data and identify the trend of quality change. Draw the time series graph of the light regulation quality evaluation index to observe its trend. For example, if it is found that the light regulation quality has significantly improved in a certain period of time, it may be related to the implemented repair strategy or user feedback. By analyzing these changes, key factors affecting the quality of the light environment can be identified. In addition, the sliding average method is used to smooth the data to eliminate short-term fluctuations and better reveal long-term trends. For example, use a 3-month sliding window to calculate the average value of the light regulation quality to highlight the evolution characteristics of the light environment quality. The analysis results will provide data support for the subsequent intelligent light source feedback adjustment model. According to the light environment quality evolution characteristics and the adaptive error repair strategy, dynamic feedback reinforcement learning is carried out to build an intelligent light source feedback adjustment model. The purpose of this step is to use machine learning technology to achieve intelligent adjustment of the light source. Choose a suitable reinforcement learning algorithm (such as Q-learning or deep Q network) to train the light source adjustment model. Set the state space, including factors such as light intensity, color temperature, and user feedback. The action space includes various light source adjustment instructions, such as increasing or decreasing light intensity and adjusting color temperature. Build a reward function to encourage the model to optimize according to the light environment quality evaluation index. The reward function can be set as: reward = light regulation quality evaluation index - system error; through training, the model will learn how to choose the best adjustment action in different states to maximize the reward. Finally, after the model training is completed, model verification and testing are carried out. Evaluate the adjustment effect of the model through real-time data to ensure that it can make quick and accurate light source adjustments under different environmental conditions. This model will provide continuous optimization capability for future intelligent light source regulation, improving overall user experience and the comfort of the light environment.

[0104] In this embodiment, an automatic light source adjustment system based on real-time environmental changes is provided for performing the automatic light source adjustment method based on real-time environmental changes as described above, comprising:

[0105] The light environment perception module is used to collect the environmental light monitoring parameters of the automatic light source, and to perform real-time light path tracking and dynamic light environment perception modeling to construct a real-time light environment perception map.

[0106] An adaptive light source adjustment module is configured to obtain an automatic light source historical operation report, perform light source parameter preference mining, and perform adaptive light source adjustment based on a real-time light environment perception map, thereby constructing an initial light source regulation strategy;

[0107] A disturbance detection module is configured to perform light source control execution based on the initial light source regulation strategy, and perform spatial light source disturbance detection, thereby generating multi-dimensional features of the disturbed light source;

[0108] An anti-interference regulation module is configured to perform light source change situation evolution analysis on the multi-dimensional features of the disturbed light source, and perform intelligent light disturbance reverse compensation, thereby constructing an intelligent anti-interference light source regulation strategy;

[0109] A time delay module is configured to perform dynamic light source parameter regulation and expected response speed delay calculation based on the intelligent anti-interference light source regulation strategy, thereby obtaining a light source response time delay value;

[0110] A feedback adjustment module is configured to perform adaptive error repair decision-making based on the light source response time delay value, and perform dynamic feedback reinforcement learning, thereby constructing an intelligent light source feedback adjustment model.

[0111] Therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application being defined by the appended claims and not by the above description, and it is intended to encompass all variations falling within the meaning and the scope of the equivalent elements of the application file.

[0112] The above description is merely that of specific embodiments of the present application, enabling a person skilled in the art to understand or implement the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application shall not be limited to these embodiments shown herein, but shall accord with the widest scope consistent with the principles and novel features developed herein.

Claims

1. A method for automatically adjusting light sources based on real-time environmental changes, characterized in that: The following steps are involved: Step S1: Collecting ambient light monitoring parameters of the automated light source, performing real-time ray path tracing and dynamic light environment perception modeling, and constructing a real-time light environment perception map; Step S2: Obtain the historical operation report of the automated light source, conduct light source parameter preference mining, and perform adaptive light source adjustment based on the real-time light environment perception map to construct an initial light source control strategy; Step S3: performing light source control according to the initial light source control strategy, and performing spatial light source disturbance detection to generate multi-dimensional features of the disturbance light source; Step S4: Analyze the evolution of the light source change trend based on the multi-dimensional characteristics of the disturbing light source, perform intelligent reverse compensation for the light disturbance, and build an intelligent anti-interference light source control strategy; Step S5: performing dynamic light source parameter control and expected response speed delay calculation based on the intelligent anti-interference light source control strategy to obtain a light source response time delay value; Step S6: making an adaptive error repair decision based on the light source response time delay value, and then performing dynamic feedback reinforcement learning to build an intelligent light source feedback adjustment model; Among them, the specific steps of step S3 are: Execute light source control according to the initial light source control strategy, monitor the light intensity in the entire space, and obtain a full range of light monitoring images; Perform multi-scale wavelet decomposition on the omnidirectional illumination monitoring map to extract illumination frequency domain maps at different scales; Performing scale-by-scale high-frequency interference detection on the illumination frequency domain map to identify light source interference points in the space; Perform abnormal light source fluctuation analysis on the light source interference points in the space to obtain the abnormal light source factors in the space; Calculating the real-time fluctuation frequency, fluctuation intensity, and fluctuation direction of the spatial abnormal light source factor to obtain multi-dimensional characteristics of the disturbance light source; The specific steps of step S4 are: Perform light source change trend evolution analysis on the multi-dimensional characteristics of the disturbing light source and extract the evolution characteristics of the disturbing light source trend; Predict the development of disturbance trajectory based on the evolution characteristics of disturbance light source situation and build a disturbance light source trajectory evolution model; Based on the disturbance light source trajectory evolution model, dynamic light source mapping is performed on the real-time light environment perception map to obtain the light environment disturbance light source mapping map; Based on the light environment disturbance light source mapping diagram, the initial light source control strategy is intelligently compensated for light disturbances and an intelligent anti-interference light source control strategy is constructed.

2. The automatic light source adjustment method based on real-time environmental changes according to claim 1, characterized in that: The specific steps of step S1 are: Continuously collect ambient light monitoring parameters of automated light sources based on a distributed light environment sensor array; Performing real-time ray path tracing on the ambient light monitoring parameters to obtain a spatial light propagation trajectory; Perform light path distribution analysis on the spatial light propagation trajectory and construct a light path distribution map; Calculating the light intensity value of the ambient light monitoring parameter; Performing a time domain change trend analysis on the light intensity value to construct a light intensity trend change curve; Based on the light path distribution diagram, the light intensity trend change curve is dynamically modeled to construct a real-time light environment perception map.

3. The automatic light source adjustment method based on real-time environmental changes according to claim 1, characterized in that: The specific steps of step S2 are: Obtain the historical operation report of the automated light source; identify the user's light adjustment behavior based on the historical operation report of the automated light source, and extract all the user's light adjustment behavior data; Calculating the light intensity and color temperature adjusted by the user light adjustment behavior data to obtain a light adjustment behavior feature; Calculating the timestamp of the user's light adjustment behavior data to obtain the time node of each light adjustment behavior; Mining light source parameter preferences based on the light adjustment behavior characteristics at the time nodes to obtain a personalized light source parameter preference pattern; Perform light source adjustment priority analysis based on the personalized light source parameter preference mode, perform priority sorting processing, and generate a personalized light source parameter configuration matrix; The personalized light source parameter configuration matrix is ​​adaptively adjusted according to the real-time light environment perception map to construct the initial light source control strategy.

4. The automatic light source adjustment method based on real-time environmental changes according to claim 3, characterized in that: The specific steps of adaptively adjusting the personalized light source parameter configuration matrix according to the real-time light environment perception map to construct the initial light source control strategy are as follows: identifying a user's current activity state based on the user's light adjustment behavior data; Analyze the light source brightness requirements based on the user's current activity status to obtain the current user's light source brightness requirements; According to the current user light source brightness requirement, the ambient light source brightness requirement deviation is calculated for the real-time light environment perception map to obtain the current ambient light brightness requirement value; The personalized light source parameter configuration matrix is ​​adaptively adjusted according to the brightness requirement value to construct the initial light source control strategy.

5. The automatic light source adjustment method based on real-time environmental changes according to claim 1, characterized in that: The specific steps of step S5 are: Dynamic light source parameter control is performed based on intelligent anti-interference light source control strategy, and light source operation control parameters are calculated; Divide the light source operation control parameters into multiple time point light source parameters to obtain the light source parameter sequence at multiple time points; Calculating the light source control response time based on the light source parameter sequence to obtain the response duration of the light source disturbance compensation control; Based on the preset control response reference time, the expected response speed delay calculation is performed on the response time of the light source disturbance compensation control to obtain the light source response time delay value.

6. The automatic light source adjustment method based on real-time environmental changes according to claim 1, characterized in that: The specific steps of step S6 are: Perform light source system error attribution analysis based on the light source response time delay value to obtain the light source system error factor; Make adaptive error repair decisions based on light source system error factors to obtain an adaptive error repair strategy; Perform light control quality assessment on light source operation control parameters and extract light environment control quality assessment index; Conduct temporal analysis of the light environment control quality assessment index to obtain the evolution characteristics of light environment quality; Dynamic feedback reinforcement learning is performed based on the evolution characteristics of light environment quality and adaptive error repair strategies to construct an intelligent light source feedback adjustment model.

7. An automatic light source adjustment method system based on real-time environmental changes, characterized in that: The method for automatically adjusting light sources based on real-time environmental changes according to claim 1 comprises: The light environment perception module is used to collect ambient light monitoring parameters of automated light sources, perform real-time ray path tracing and dynamic light environment perception modeling, and build a real-time light environment perception map; The adaptive light source adjustment module is used to obtain historical operation reports of automated light sources, conduct light source parameter preference mining, and perform adaptive light source adjustments based on the real-time light environment perception map to build an initial light source control strategy; The disturbance detection module is used to perform light source control according to the initial light source control strategy, perform spatial light source disturbance detection, and generate multi-dimensional features of the disturbance light source; The anti-interference control module is used to analyze the evolution of light source changes based on the multi-dimensional characteristics of the disturbing light source, perform intelligent reverse compensation for light disturbances, and build an intelligent anti-interference light source control strategy; The time delay module is used to perform dynamic light source parameter control and expected response speed delay calculation based on the intelligent anti-interference light source control strategy to obtain the light source response time delay value; The feedback adjustment module is used to make adaptive error repair decisions based on the light source response time delay value, and then perform dynamic feedback reinforcement learning to build an intelligent light source feedback adjustment model.

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