LED Lighting Efficiency Optimization Methods and Systems

Abstract

This invention relates to the field of lighting control technology, specifically to a method and system for optimizing LED lighting efficiency. The method includes the following steps: based on real-time external environmental data and historical LED lighting operation data, identifying the changing trends of lighting usage patterns and environmental conditions, and obtaining environmental and operational data analysis results. This invention uses a Gaussian process regression model to predict and analyze LED lighting demand, achieving accurate prediction of lighting demand under different environmental conditions and time periods, improving the responsiveness and adaptability of lighting equipment. By comprehensively considering real-time external environmental data and historical lighting operation data, the lighting control decision-making process is optimized. The introduction of a hybrid dynamic Bayesian network enhances the real-time assessment capability of the energy efficiency status of LED lighting equipment, making energy efficiency management more refined and efficient. The use of multi-objective optimization strategies provides a more comprehensive solution for balancing energy efficiency, light quality, and user comfort.

Description

Technical Field

[0001] This invention relates to the field of lighting control technology, and in particular to methods and systems for optimizing LED lighting efficiency. Background Technology

[0002] The field of lighting control technology involves automated systems and methods for regulating and controlling the brightness, color temperature, and energy consumption of indoor and outdoor lighting. The aim is to improve energy efficiency, enhance user experience, and adapt to the lighting needs of different environments. Through intelligent control systems, such as sensors and timers, lighting control technology can automatically adjust lighting parameters based on changes in natural light, space usage, or specific activity requirements, thereby achieving energy savings and enhanced comfort.

[0003] Among these methods, LED lighting efficiency optimization focuses on improving the performance and energy efficiency of LED lighting devices. The aim is to reduce energy consumption, extend the lifespan of lighting equipment, and ensure that light quality meets user needs. High efficiency and high light output are achieved through thermal management, optical adjustment, and intelligent control strategies, providing cost-effective and environmentally friendly lighting solutions.

[0004] Traditional lighting control methods lack the ability to dynamically adapt to environmental changes and user needs, resulting in low lighting efficiency and inefficient energy utilization. For example, traditional methods cannot precisely adjust brightness and color temperature to meet lighting needs at different times of day, or effectively utilize natural light, leading to unnecessary energy consumption and a reduced user experience. The lack of an effective real-time energy efficiency assessment mechanism makes it difficult for lighting methods to adjust strategies in a timely manner to cope with changes in equipment performance or fluctuations in the external environment, thus affecting the overall energy efficiency and continuous performance of lighting equipment. Summary of the Invention

[0005] The purpose of this invention is to address the shortcomings of existing technologies by proposing a method and system for optimizing LED lighting efficiency.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an LED lighting efficiency optimization method, comprising the following steps:

[0007] S1: Based on real-time external environment data and historical LED lighting operation data, identify lighting usage patterns and environmental condition change trends to obtain environmental and operation data analysis results;

[0008] S2: Based on the analysis results of the environmental and operational data, a Gaussian process regression model is used to predict the lighting demand under various weather conditions, time periods and spatial areas, and the lighting demand prediction analysis results are obtained.

[0009] S3: Based on the lighting demand prediction and analysis results, dynamically adjust the LED lighting driving current, PWM duty cycle and color temperature control parameters to obtain a real-time lighting adjustment strategy;

[0010] S4: Based on the real-time lighting adjustment strategy, combined with the energy consumption data of the LED lamp group, indoor temperature and humidity and illuminance feedback data, the energy efficiency of the LED lighting equipment is evaluated in real time using a hybrid dynamic Bayesian network to obtain the dynamic energy efficiency evaluation result.

[0011] S5: Based on the aforementioned dynamic energy efficiency assessment results, an optimized lighting control strategy is obtained by balancing lighting power allocation, luminous flux maintenance, and thermal management parameters through multi-objective optimization.

[0012] S6: Using the optimized lighting control strategy, adjust the LED lamp dimming curve, light source arrangement mode and sensing control threshold to optimize the energy consumption of LED lighting equipment and obtain the LED lighting control implementation strategy.

[0013] The present invention improves upon the following: the environmental and operational data analysis results include changes in indoor and outdoor light intensity, temperature fluctuation range, and peak pedestrian traffic periods; the lighting demand prediction analysis results include predicted lighting intensity demand, color temperature adjustment demand, and lighting usage duration; the real-time lighting adjustment strategy includes dynamic brightness adjustment parameters, color temperature change settings, and lighting switch-on / off time plans; the energy efficiency dynamic evaluation results include energy consumption trend analysis information, efficiency improvement point identification results, and potential energy-saving areas; the optimized lighting control strategy includes energy efficiency optimization configuration, light quality improvement measures, and user comfort enhancement strategies; and the LED lighting control implementation strategy includes implemented brightness settings, color temperature adjustment schemes, and LED lighting equipment switching logic.

[0014] The present invention is improved by using a Gaussian process regression model to predict lighting demand under various weather conditions, time periods, and spatial areas based on the analysis results of the environmental and operational data. The specific steps for obtaining the lighting demand prediction and analysis results are as follows:

[0015] S201: Based on the environmental and operational data analysis results, analyze the lighting requirements under different environmental conditions and time periods, identify key lighting requirement variables, including brightness requirements and lighting usage time, and obtain the analysis results of key lighting requirement variables;

[0016] S202: Based on the analysis results of the key variables of lighting demand, analyze the lighting demand data under multiple time periods and environmental conditions, and identify the lighting demand patterns under different time periods and environmental conditions through cluster analysis to obtain the demand pattern identification results.

[0017] S203: Based on the demand pattern recognition results, a Gaussian process regression model is used to construct a lighting demand prediction model that matches differentiated environmental conditions and time periods. The model is then used to analyze and predict lighting demand under differentiated environmental conditions and time periods, and the lighting demand prediction analysis results are obtained.

[0018] The present invention improves upon the Gaussian process regression model, according to formula I:

[0019] ;

[0020] in, Kernel function, For input points, For another input point, For length scale parameters, This is the distance adjustment factor. This is the environmental adaptability adjustment factor. express and The distance between them.

[0021] According to Formula II:

[0022] ;

[0023] The lighting demand at each predicted point is calculated to obtain the lighting demand prediction analysis results, where, For input points, For predicted values, Let covariance be the covariance matrix at the training points. For noise level, Given the known training target value, This is a matrix adjusted based on environmental factors. These are the matrix weight coefficients. express transpose, Represents a vector. Represents the covariance matrix Identity matrices of the same dimension.

[0024] The present invention improves upon the above-mentioned lighting demand prediction and analysis results by dynamically adjusting the LED lighting drive current, PWM duty cycle, and color temperature control parameters to obtain a real-time lighting adjustment strategy. The specific steps are as follows:

[0025] S301: Based on the lighting demand prediction and analysis results, compare the existing LED lighting configuration with the predicted demand, analyze the brightness, color temperature and switching status, identify the lighting parameters that need to be adjusted, and obtain the configuration and demand difference analysis results;

[0026] S302: Based on the configuration and demand difference analysis results, adjust the brightness and color temperature of the LED lighting equipment, calculate and set the switching time points, optimize the lighting quality of the LED lighting equipment, and obtain the adjusted lighting configuration;

[0027] S303: Based on the adjusted lighting configuration, the energy efficiency of the LED lighting equipment is optimized by adjusting the configuration of the LED lighting equipment to obtain a real-time lighting adjustment strategy.

[0028] The present invention improves upon this invention by using a hybrid dynamic Bayesian network to evaluate the energy efficiency of LED lighting equipment in real time, based on the aforementioned real-time lighting adjustment strategy and combined with LED lamp group energy consumption data, indoor temperature and humidity feedback data, and illuminance feedback data. The specific steps for obtaining the dynamic energy efficiency evaluation result are as follows:

[0029] S401: Based on the real-time lighting adjustment strategy, collect real-time operating data of LED lighting equipment, including energy consumption, switching frequency, and brightness level, and record environmental variables, including indoor and outdoor temperature and humidity, to obtain lighting operation and environmental status records;

[0030] S402: Based on the recorded lighting operation and environmental status, perform data analysis to analyze the correlation between the energy efficiency status of LED lighting equipment and environmental variables, identify key environmental factors that lead to changes in energy efficiency, and obtain the results of energy efficiency correlation analysis.

[0031] S403: Based on the energy efficiency correlation analysis results, a hybrid dynamic Bayesian network is used to analyze the changes in the operating data of LED lighting equipment and environmental variables, and to dynamically evaluate the energy efficiency status of LED lighting equipment to obtain the dynamic energy efficiency evaluation results.

[0032] The present invention improves upon this invention by stating that the hybrid dynamic Bayesian network is configured according to the formula:

[0033] ;

[0034] Calculate the state probability at the next time point to generate dynamic energy efficiency assessment results, where... Indicates at a point in time Continuous energy efficiency data, Indicates at a point in time The operating status of discrete equipment This represents the environmental factor parameters at time point t. Indicates at a point in time The predicted value of continuous energy efficiency data, Indicates at a point in time The predicted value of the operating status of discrete equipment. Represents from time point 1 to time point 2. A sequence of continuous energy efficiency data, Represents from time point 1 to time point 2. A sequence of discrete device operating states.

[0035] The present invention improves upon the above-mentioned dynamic energy efficiency evaluation results by balancing lighting power allocation, luminous flux maintenance, and thermal management parameters through multi-objective optimization, and obtains the following specific steps for optimizing the lighting control strategy:

[0036] S501: Based on the energy efficiency dynamic evaluation results, analyze the difference between the current LED lighting equipment control parameters and the expected energy efficiency target, and combine brightness, color temperature and energy consumption factors to identify the priority and scope of adjustment, and generate lighting parameter adjustment analysis results;

[0037] S502: Based on the lighting parameter adjustment analysis results, formulate a multi-objective optimization scheme to balance energy efficiency, light quality and user comfort, and obtain a multi-objective optimization scheme for LED lighting equipment;

[0038] S503: Based on the multi-objective optimization scheme of the LED lighting equipment, formulate optimized lighting control parameter settings, including brightness, color temperature and usage time, optimize energy efficiency and user experience, and obtain an optimized lighting control strategy.

[0039] An LED lighting efficiency optimization system, the system comprising:

[0040] The data integration module collects and integrates information based on real-time external environment data and historical LED lighting operation data, analyzes the relationship between the efficiency of LED lighting equipment and the external environment, and obtains the results of efficiency-environment relationship analysis.

[0041] Based on the efficiency-environment relationship analysis results, the lighting trend analysis module analyzes the changing trends of lighting usage patterns and environmental conditions, and obtains the trend analysis results.

[0042] Based on the trend analysis results, the lighting demand analysis module analyzes lighting demand under different environmental conditions and time periods, identifies key variables, and obtains lighting demand prediction results.

[0043] Based on the lighting demand prediction results, the configuration adjustment module analyzes the difference between the current lighting configuration and the predicted demand, dynamically adjusts the LED lighting parameters to match the lighting demand, and obtains the adjusted lighting configuration.

[0044] Based on the adjusted lighting configuration, the energy efficiency analysis module collects operating data and environmental variables, analyzes the energy efficiency status of LED lighting equipment, and obtains dynamic energy efficiency evaluation results.

[0045] Based on the energy efficiency dynamic evaluation results, the optimization strategy module balances energy efficiency, light quality, and user comfort through a multi-objective optimization process to obtain an optimized lighting control strategy.

[0046] Based on the optimized lighting control strategy, the control implementation module adjusts the LED lighting equipment settings to match actual needs, performs performance evaluation and parameter tuning, and obtains the LED lighting control implementation strategy.

[0047] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0048] In this invention, a Gaussian process regression model is used to predict and analyze LED lighting demand, achieving accurate prediction of lighting demand under different environmental conditions and time periods. This improves the responsiveness and adaptability of lighting equipment. By comprehensively considering real-time external environmental data and historical lighting operation data, the lighting control decision-making process is optimized, making lighting configuration more in line with actual needs, reducing energy waste and improving user experience. The introduction of a hybrid dynamic Bayesian network enhances the real-time assessment capability of the energy efficiency status of LED lighting equipment, making energy efficiency management more refined and efficient. The use of multi-objective optimization strategies provides a more comprehensive solution in balancing energy efficiency, light quality, and user comfort. Attached Figure Description

[0049] Figure 1 The flowchart of the LED lighting efficiency optimization method proposed in this invention is shown below;

[0050] Figure 2 This is a detailed flowchart of step S1 in the LED lighting efficiency optimization method proposed in this invention;

[0051] Figure 3 This is a detailed flowchart of step S2 in the LED lighting efficiency optimization method proposed in this invention;

[0052] Figure 4 This is a detailed flowchart of step S3 in the LED lighting efficiency optimization method proposed in this invention;

[0053] Figure 5 This is a detailed flowchart of step S4 in the LED lighting efficiency optimization method proposed in this invention;

[0054] Figure 6 This is a detailed flowchart of step S5 in the LED lighting efficiency optimization method proposed in this invention;

[0055] Figure 7 This is a detailed flowchart of step S6 in the LED lighting efficiency optimization method proposed in this invention;

[0056] Figure 8 This invention proposes a module diagram for an LED lighting efficiency optimization system. Detailed Implementation

[0057] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0058] In the description of this invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicating orientation or positional relationships, are based on the orientation or positional relationships shown in the accompanying drawings and are only for the convenience of describing the invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Furthermore, in the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0059] Example

[0060] Please see Figure 1 This invention provides a technical solution: an LED lighting efficiency optimization method, comprising the following steps:

[0061] S1: Based on real-time external environment data and historical LED lighting operation data, identify lighting usage patterns and environmental condition change trends to obtain environmental and operation data analysis results;

[0062] S2: Based on the analysis results of environmental and operational data, a Gaussian process regression model is used to predict lighting demand under various weather conditions, time periods, and spatial areas, and the lighting demand prediction and analysis results are obtained.

[0063] S3: Based on the lighting demand prediction and analysis results, dynamically adjust the LED lighting drive current, PWM duty cycle and color temperature control parameters to obtain a real-time lighting adjustment strategy;

[0064] S4: Based on the real-time lighting adjustment strategy, combined with the energy consumption data of LED lamp groups, indoor temperature and humidity and illuminance feedback data, a hybrid dynamic Bayesian network is used to evaluate the energy efficiency of LED lighting equipment in real time and obtain the dynamic energy efficiency evaluation results.

[0065] S5: Based on the dynamic energy efficiency assessment results, an optimized lighting control strategy is obtained by balancing lighting power distribution, luminous flux maintenance and thermal management parameters through multi-objective optimization.

[0066] S6: By optimizing the lighting control strategy, adjusting the dimming curve of LED lamps, the light source arrangement mode and the sensing control threshold, the energy consumption of LED lighting equipment is optimized, and the LED lighting control implementation strategy is obtained.

[0067] The environmental and operational data analysis results include changes in indoor and outdoor light intensity, temperature fluctuation range, and peak pedestrian traffic periods. The lighting demand prediction analysis results include predicted lighting intensity demand, color temperature adjustment demand, and lighting usage duration. The real-time lighting adjustment strategies include dynamic brightness adjustment parameters, color temperature change settings, and lighting switch-on / off time plans. The energy efficiency dynamic assessment results include energy consumption trend analysis information, efficiency improvement point identification results, and potential energy-saving areas. The optimized lighting control strategies include energy efficiency optimization configuration, light quality improvement measures, and user comfort enhancement strategies. The LED lighting control implementation strategies include implemented brightness settings, color temperature adjustment schemes, and LED lighting equipment switching logic.

[0068] Please see Figure 2 Based on real-time external environmental data and historical LED lighting operation data, the specific steps for identifying lighting usage patterns and environmental condition change trends to obtain environmental and operational data analysis results are as follows:

[0069] S101: Based on real-time external environment data and historical LED lighting operation data, including weather changes, sunshine duration, brightness adjustment records and switching frequency, the specific process of collecting and integrating data, evaluating the relationship between the utilization efficiency of LED lighting equipment and the external environment, and generating data integration and analysis results is as follows:

[0070] In sub-step S101, data collection and integration are performed based on real-time external environment data and historical LED lighting operation data. Data preprocessing techniques are used, and detailed processing is performed using Python's pandas library. Relevant data is extracted from the database using SQL query statements. Pandas is used to merge data, handle missing values, and remove outliers to ensure the accuracy and completeness of the data, and generate data integration and analysis results.

[0071] S102: Based on the data integration and analysis results, through statistical analysis, identify the changing trends of lighting usage patterns and environmental conditions, including fluctuations in light intensity and energy consumption, and obtain the specific process for the trend analysis results;

[0072] In sub-step S102, based on the data integration and analysis results, statistical analysis is performed. Time series analysis technology is used, and the autoregressive differential moving average model is applied through the statsmodels library of Python to perform time series analysis. Through time series modeling, trend estimation and seasonal adjustment, accurate trend prediction and analysis of light intensity and energy consumption fluctuations are performed, and trend analysis results are generated.

[0073] S103: Based on the trend analysis results, the specific process for assessing the correlation between current and past lighting efficiency and environmental conditions, analyzing the efficiency of LED lighting equipment usage patterns, and obtaining environmental and operational data analysis results is as follows;

[0074] In sub-step S103, based on the trend analysis results, the correlation between lighting efficiency and environmental conditions is assessed. Linear regression analysis is used, implemented in R language, and the lm() function is used to construct a linear regression model. Variable selection, model fitting, and hypothesis testing are performed to analyze and evaluate the correlation between lighting usage patterns and environmental conditions, generating environmental and operational data analysis results.

[0075] Please see Figure 3 Based on the analysis results of environmental and operational data, a Gaussian process regression model is used to predict lighting demand under various weather conditions, time periods, and spatial areas. The specific steps for obtaining the lighting demand prediction and analysis results are as follows:

[0076] S201: Based on the analysis results of environmental and operational data, analyze the lighting needs under different environmental conditions and time periods, identify key lighting demand variables, including brightness requirements and lighting usage time, and obtain the specific process for analyzing the key variables of lighting needs.

[0077] In sub-step S201, based on the results of environmental and operational data analysis, a detailed analysis of lighting demand variables is performed. Data filtering and feature engineering are carried out using Python and pandas libraries. SQL statements are used to extract lighting-related time-series data from a large database, such as brightness adjustment records, switching frequency, and indoor and outdoor temperature and light intensity. Pandas is used for data cleaning and merging, and statistical indicators such as mean and variance are calculated. Key variables of brightness demand and lighting usage time are identified, and the analysis results of key variables of lighting demand are generated.

[0078] S202: Based on the analysis results of key variables of lighting demand, analyze the lighting demand data under multiple time periods and environmental conditions, and identify lighting demand patterns under different time periods and environmental conditions through cluster analysis. The specific process for obtaining the demand pattern identification results is as follows:

[0079] In sub-step S202, based on the analysis results of key variables of lighting demand, lighting demand patterns are identified. Advanced clustering analysis techniques are used, and the K-Means clustering algorithm is executed using Python's scikit-learn library. Data preprocessing, such as standardization and dimensionality reduction, is performed to ensure the accuracy of clustering analysis. Lighting demand data under different time periods and environmental conditions are systematically classified to identify lighting demand patterns under each time period and environmental condition, generating demand pattern identification results.

[0080] S203: Based on the demand pattern recognition results, a Gaussian process regression model is used to construct a lighting demand prediction model that matches differentiated environmental conditions and time periods. The specific process for analyzing and predicting lighting demand under differentiated environmental conditions and time periods and obtaining lighting demand prediction analysis results is as follows:

[0081] In sub-step S203, based on the demand pattern recognition results, predictive analysis of lighting demand is performed. A Gaussian process regression model is adopted, and the model is configured and trained using the scikit-learn library of Python. The kernel function and hyperparameters of the Gaussian process regression model are set, and cross-validation is performed to evaluate the predictive performance of the model. The lighting demand under different environmental conditions and time periods is accurately analyzed and predicted, and lighting demand prediction analysis results are generated.

[0082] Gaussian process regression model, according to formula I:

[0083] ;

[0084] in, Kernel function, used to calculate input points and Similarity between them For input points, a data vector represents an environmental or lighting condition. As another input point, used to connect with The data vectors being compared The length scale parameter controls the kernel function's sensitivity to the distance between input points. This is a distance adjustment factor used to adjust the input point. and Distance between The role of the kernel function is to help the model adjust its predictions based on changes in the distance between points, thus more accurately reflecting the lighting needs under different spatial configurations. An environmental adaptability adjustment coefficient provides additional adjustment capabilities, allowing the model to better adapt to different lighting environments and needs. By adjusting this coefficient, the model can more flexibly adapt to changes in lighting requirements under various environmental conditions. express and The distance between them.

[0085] According to Formula II:

[0086] ;

[0087] The lighting demand at each predicted point is calculated to obtain the lighting demand prediction analysis results, where, For input points, a data vector represents an environmental or lighting condition. The predicted value, i.e., given the input Lighting demand forecasting The covariance matrix at the training points reflects the similarity between the training data points. The noise level represents the amount of noise considered in the prediction. The training target value is the historical lighting demand data. This is a matrix adjusted for environmental factors, used to reflect the impact of environmental changes on lighting demand. The matrix weight coefficients determine the matrix. The importance of forecasting is determined numerically through data analysis methods to best reflect the impact of environmental factors on lighting demand. express transpose, Represents a vector that includes input points. Compared to the kernel function value of each point in the training set, Represents the covariance matrix Identity matrices of the same dimension.

[0088] The execution process of the improved formula is as follows:

[0089] Setting the length scale parameter in the kernel function And introduce new parameters and To enhance the model's adaptability to different environmental conditions, Adjust different distances Impact on kernel function Provides additional flexibility to adapt to various lighting needs through kernel functions. Calculate input points and The similarity between them is calculated by constructing a covariance matrix using a kernel function. An adjustment matrix is ​​introduced into the prediction formula. and weighting coefficients In order to take into account the impact of environmental changes on lighting needs, Adjust according to environmental factors such as temperature and humidity. The optimal value is determined through data-driven methods, such as cross-validation, and then included in the predicted value. Calculate and generate lighting demand forecasting analysis results.

[0090] Please see Figure 4 Based on the lighting demand prediction and analysis results, the specific steps of dynamically adjusting the LED lighting drive current, PWM duty cycle, and color temperature control parameters to obtain the real-time lighting adjustment strategy are as follows:

[0091] S301: Based on the lighting demand forecast analysis results, compare the existing LED lighting configuration with the forecast demand, analyze the brightness, color temperature and on / off status, identify the lighting parameters that need to be adjusted, and obtain the configuration and demand difference analysis results. The specific process is as follows:

[0092] In sub-step S301, based on the lighting demand prediction analysis results, a difference analysis between LED lighting configuration and demand is performed. Data analysis technology is used, and data comparison is performed through Python's pandas library. Current lighting configuration data, such as brightness level, color temperature setting, and on / off status, is extracted from the database and accurately compared with the predicted lighting demand data. Conditional statements are used to identify the parameters that need to be adjusted, and the difference analysis results between configuration and demand are generated.

[0093] S302: Based on the analysis results of configuration and demand differences, adjust the brightness and color temperature of LED lighting equipment, calculate and set the switching time points, optimize the lighting quality of LED lighting equipment, and obtain the specific process of the adjusted lighting configuration;

[0094] In sub-step S302, based on the configuration and demand difference analysis results, the brightness and color temperature of the LED lighting equipment are adjusted. An intelligent control algorithm is adopted, and an automated script is written to adjust the lighting parameters according to the difference analysis results through the interaction between the Python script and the API interface of the IoT platform. The optimal switching time is calculated to optimize the lighting quality. Logical judgments and loops are set to adjust the lighting equipment configuration in real time, ensuring that the lighting system automatically adapts to the predicted demand and generates the adjusted lighting configuration.

[0095] S303: Based on the adjusted lighting configuration, the specific process of obtaining the real-time lighting adjustment strategy by adjusting the configuration of LED lighting equipment and optimizing the energy efficiency of LED lighting equipment is as follows;

[0096] In sub-step S303, based on the adjusted lighting configuration, the energy efficiency of the LED lighting equipment is optimized. Energy efficiency analysis technology is used, and energy consumption is simulated and analyzed using MATLAB. The Simulink toolbox of MATLAB is used to simulate the energy consumption scenario of the lighting system. The energy efficiency performance is evaluated according to the adjusted configuration, optimization algorithms such as genetic algorithms or particle swarm optimization are executed, and lighting parameters are automatically adjusted to achieve optimal energy efficiency, generating a real-time lighting adjustment strategy.

[0097] Please see Figure 5 Based on the real-time lighting adjustment strategy, and combined with LED light group energy consumption data, indoor temperature and humidity feedback data, a hybrid dynamic Bayesian network is used to evaluate the energy efficiency of LED lighting equipment in real time, and the specific steps to obtain the dynamic energy efficiency evaluation results are as follows:

[0098] S401: Based on the real-time lighting adjustment strategy, collect real-time operating data of LED lighting equipment, including energy consumption, switching frequency, and brightness level, and record environmental variables, including indoor and outdoor temperature and humidity. The specific process for recording lighting operation and environmental status is as follows:

[0099] In sub-step S401, based on the real-time lighting adjustment strategy, the Pandas library integrated into Python is used to collect and process real-time operating data of LED lighting equipment, including energy consumption data, equipment switching frequency, and brightness level measurements. Environmental variable data, such as indoor and outdoor temperature and humidity values, are recorded simultaneously. Pandas' data frame functionality is used to store and organize multidimensional data, facilitating subsequent analysis and generating records of lighting operation and environmental status.

[0100] S402: Based on lighting operation and environmental status records, perform data analysis to analyze the correlation between the energy efficiency status of LED lighting equipment and environmental variables, identify key environmental factors that lead to changes in energy efficiency, and obtain the specific process of energy efficiency correlation analysis results;

[0101] In substep S402, based on lighting operation and environmental status records, statistical analysis methods are employed. Correlation analysis is performed using Python's SciPy library to reveal the relationship between the energy efficiency status of LED lighting equipment and various environmental variables. By calculating Spearman or Pearson correlation coefficients, key environmental factors significantly affecting LED lighting energy efficiency are identified. This analysis helps to understand changes in energy efficiency performance under different environmental conditions, thereby generating energy efficiency correlation analysis results.

[0102] S403: Based on the results of energy efficiency correlation analysis, a hybrid dynamic Bayesian network is used to analyze the changes in the operating data of LED lighting equipment and environmental variables, and to dynamically evaluate the energy efficiency status of LED lighting equipment to obtain the specific process of dynamic energy efficiency evaluation results.

[0103] In substep S403, based on the energy efficiency correlation analysis results, a hybrid dynamic Bayesian network is employed, utilizing the PGMPY library in Python for network construction and computation. This includes defining the network structure, parameter learning, and employing appropriate inference algorithms to dynamically assess the energy efficiency status of LED lighting equipment. The network model is continuously updated based on historical and real-time data to reflect the latest relationship between equipment status and environmental variables, thereby generating dynamic energy efficiency assessment results.

[0104] Hybrid dynamic Bayesian networks, according to the formula:

[0105] ;

[0106] Calculate the state probability at the next time point to generate dynamic energy efficiency assessment results, where... Indicates at a point in time Continuous energy efficiency data, Indicates at a point in time The operating status of discrete equipment This refers to environmental factor parameters at time point t, such as temperature or humidity. Indicates at a point in time The predicted value of continuous energy efficiency data, Indicates at a point in time The predicted value of the operating status of discrete equipment. Represents a given time point When considering continuous energy efficiency data, discrete equipment operating status, and environmental factors, the time point... The conditional probability distribution of continuous energy efficiency data. Represents a given time point When considering the operating status and environmental factors of discrete equipment, the time point The conditional probability distribution of the operating state of discrete devices. Represents from time point 1 to time point 2. A sequence of continuous energy efficiency data, including energy efficiency data at each point in time. It reflects the historical continuous energy efficiency status of LED lighting equipment from the past to the present. Represents from time point 1 to time point 2. A sequence of discrete device operating states, containing the device state at each point in time. It showcases the history of the equipment's operational status from the past to the present.

[0107] The specific execution process of the improved formula is as follows:

[0108] Initialize the structure of the hybrid dynamic Bayesian network, including defining the nodes and edges in the network. Nodes represent variables, including continuous energy efficiency data. Discrete equipment operating status and newly added environmental factor parameters Edges represent the dependencies between variables. Parameter learning is performed using historical datasets and methods such as maximum likelihood estimation or Bayesian estimation to learn and update the conditional probability distribution in the network. and The conditional probability distribution expresses the probability of the state at the next time step given the current state and environmental factors. It uses real-time data for inference, based on the current state. , and environmental factors Calculate future states using learned models and Based on the probability distribution, Bayesian inference is used, along with algorithms such as particle filtering or Kalman filtering, to generate a dynamic assessment result of the energy efficiency status of LED lighting equipment at the next time point.

[0109] Please see Figure 6 Based on the dynamic energy efficiency assessment results, and through multi-objective optimization, balancing lighting power allocation, luminous flux maintenance, and thermal management parameters, the specific steps for optimizing the lighting control strategy are as follows:

[0110] S501: Based on the dynamic energy efficiency assessment results, analyze the differences between the current control parameters of LED lighting equipment and the expected energy efficiency targets, combine brightness, color temperature and energy consumption factors, identify the priority and scope of adjustment, and generate the specific process of lighting parameter adjustment analysis results;

[0111] In sub-step S501, based on the dynamic energy efficiency assessment results, a multiple linear regression algorithm is adopted, and NumPy and SciPy libraries are used for parameter optimization and variance analysis. The model structure is defined, including setting the regression coefficients and intercepts. The least squares method is used to calculate the optimal solution, the optimizer is set to gradient descent, the loss function is selected as mean square error, and the lighting parameter adjustment analysis results are generated.

[0112] S502: Based on the analysis results of lighting parameter adjustment, a multi-objective optimization scheme is formulated to balance energy efficiency, light quality and user comfort. The specific process for obtaining the multi-objective optimization scheme for LED lighting equipment is as follows:

[0113] In sub-step S502, based on the analysis results of lighting parameter adjustment, a multi-objective optimization algorithm is adopted. The optimization process is implemented through the SciPy library of Python. It considers three objectives: energy efficiency, light quality, and user comfort. Corresponding optimization objective functions and constraints are set. The parameters in the algorithm, such as the mutation rate and crossover rate, are adjusted iteratively. During the solution process, the parameters are continuously adjusted to find the optimal solution and generate a multi-objective optimization scheme for LED lighting equipment.

[0114] S503: Based on the multi-objective optimization scheme for LED lighting equipment, formulate optimized lighting control parameter settings, including brightness, color temperature and usage time, optimize energy efficiency and user experience, and obtain the specific process of the optimized lighting control strategy.

[0115] In sub-step S503, based on the multi-objective optimization scheme of LED lighting equipment, a fuzzy logic control algorithm is adopted. The Skfuzzy library is used to define fuzzy variables and rule sets, and fuzzy rules are set. The input variables are brightness error and color temperature error, and the output variable is the control command adjustment amount. The maximum-minimum inference mechanism and centroid defuzzification method are applied to optimize the LED lighting control parameters and generate an optimized lighting control strategy.

[0116] Please see Figure 7 By optimizing lighting control strategies, adjusting the dimming curves of LED luminaires, the light source arrangement mode, and the sensing control threshold, the energy consumption of LED lighting equipment is optimized. The specific steps for implementing LED lighting control strategies are as follows:

[0117] S601: Based on the optimized lighting control strategy, the LED lighting equipment is set and adjusted, including the configuration of light brightness, color temperature selection and switching logic, to match the current needs and environmental conditions, and the specific process of real-time adjustment of lighting settings is as follows;

[0118] In sub-step S601, based on the optimized lighting control strategy, the brightness and color temperature of the LED lighting equipment are selected and the switching logic is configured. The control is performed using Python scripts and Raspberry Pi hardware interface. The brightness is adjusted through PWM signal, the color temperature is adjusted through I2C communication protocol, and the switching operation is realized through logic control. The lighting settings are adjusted in real time to match the current needs and environmental conditions.

[0119] S602: The specific process for implementing LED lighting equipment performance monitoring based on real-time adjustment of lighting settings, conducting energy consumption and lighting effect assessments, determining the current energy efficiency and performance status of LED lighting equipment, and obtaining LED lighting equipment performance assessment results is as follows:

[0120] In sub-step S602, based on real-time adjustment of lighting settings, a real-time monitoring system is adopted, using Arduino and sensors to collect energy consumption and light data of LED lighting equipment. Time series analysis methods are applied, and the data is processed and analyzed using Python's Pandas and NumPy libraries to evaluate energy consumption and lighting effects, and generate LED lighting equipment performance evaluation results.

[0121] S603: Based on the performance evaluation results of LED lighting equipment, parameter adjustments and optimizations are made to optimize the operation of LED lighting equipment, match energy efficiency and user experience, and the specific process of obtaining the LED lighting control implementation strategy is as follows;

[0122] In sub-step S603, based on the performance evaluation results of LED lighting equipment, the simulated annealing algorithm is used to adjust and optimize parameters. The parameter configuration is evaluated by defining an energy function, the initial temperature and cooling rate are set, the optimal parameters are iteratively searched, and the operation of LED lighting equipment is optimized using a Python script to generate an LED lighting control implementation strategy.

[0123] Please see Figure 8 LED lighting efficiency optimization system, the system includes:

[0124] The data integration module collects and integrates information based on real-time external environment data and historical LED lighting operation data, analyzes the relationship between the efficiency of LED lighting equipment and the external environment, and obtains the results of efficiency-environment relationship analysis.

[0125] The lighting trend analysis module analyzes the changing trends of lighting usage patterns and environmental conditions based on the results of the efficiency-environment relationship analysis, and obtains the trend analysis results.

[0126] The lighting demand analysis module analyzes lighting demand under different environmental conditions and time periods based on the results of trend analysis, identifies key variables, and obtains lighting demand prediction results.

[0127] The configuration adjustment module analyzes the difference between the current lighting configuration and the predicted demand based on the lighting demand forecast results, and dynamically adjusts the LED lighting parameters to match the lighting demand, thus obtaining the adjusted lighting configuration;

[0128] Based on the adjusted lighting configuration, the energy efficiency analysis module collects operational data and environmental variables, analyzes the energy efficiency status of LED lighting equipment, and obtains dynamic energy efficiency assessment results.

[0129] Based on the dynamic energy efficiency assessment results, the optimization strategy module balances energy efficiency, light quality, and user comfort through a multi-objective optimization process to obtain an optimized lighting control strategy.

[0130] The control implementation module optimizes the lighting control strategy, adjusts the LED lighting equipment settings to match actual needs, conducts performance evaluation and parameter tuning, and obtains the LED lighting control implementation strategy.

[0131] The above are merely preferred embodiments of the present invention and are not intended to limit the present invention in any other way. Any person skilled in the art may make changes or modifications to the above-disclosed technical content to create equivalent embodiments that can be applied to other fields. However, any simple modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the protection scope of the present invention.

Claims

1. A method for optimizing the efficiency of LED lighting, characterized in that The method comprises the following steps: Based on real-time external environment data and historical LED lighting operation data, identify lighting usage patterns and environmental condition trends, and obtain environmental and operation data analysis results; According to the environmental and operation data analysis results, a Gaussian process regression model is used to predict lighting demand under various weather conditions, time periods and spatial regions, and obtain lighting demand prediction analysis results; According to the environmental and operation data analysis results, a Gaussian process regression model is used to predict lighting demand under various weather conditions, time periods and spatial regions, and obtain lighting demand prediction analysis results. The specific steps are as follows: Based on the environmental and operation data analysis results, analyze the lighting demand under different environmental conditions and time periods, identify the key lighting demand variables, including brightness demand and lighting usage time, and obtain lighting demand key variable analysis results; Based on the lighting demand key variable analysis results, analyze the lighting demand data under multiple time periods and environmental conditions, identify the lighting demand patterns of different time periods and environmental conditions through cluster analysis, and obtain demand pattern recognition results; Based on the demand pattern recognition results, a Gaussian process regression model is used to construct a lighting demand prediction model that matches different environmental conditions and time periods, analyze and predict lighting demand under different environmental conditions and time periods, and obtain lighting demand prediction analysis results; The Gaussian process regression model is according to formula I: ； wherein, a kernel function, is an input point, is another input point, is a length scale parameter, is a distance adjustment coefficient, is an environmental adaptability adjustment coefficient, denotes and a distance between; According to formula II: ； computing the lighting demand for each prediction point to obtain a lighting demand prediction analysis result, wherein, for an input point, for a prediction value, for a covariance matrix on a training point, for a noise level, for a known training target value, for a matrix adjusted based on environmental factors, for a matrix weight coefficient, denotes a transpose of, a vector, a unit matrix of the same dimension as the covariance matrix ​ Based on the lighting demand prediction analysis results, dynamically adjust the LED lighting driving current, PWM duty cycle and color temperature control parameters to obtain real-time lighting adjustment strategy; According to the real-time lighting adjustment strategy, combined with LED lamp group energy consumption collection data, indoor temperature and humidity and illumination feedback data, use a hybrid dynamic Bayesian network to evaluate the energy efficiency of LED lighting equipment in real time, and obtain energy efficiency dynamic evaluation results; The hybrid dynamic Bayesian network is according to formula: ； computing the state probability of the next time point, generating the energy efficiency dynamic evaluation result, wherein, represents the continuous energy efficiency data at time point , represents the discrete device running state at time point , represents the environmental factor parameter at time point t, represents the predicted value of the continuous energy efficiency data at time point , represents the predicted value of the discrete device running state at time point , represents the sequence of the continuous energy efficiency data from time point 1 to time point , represents the sequence of the discrete device running state from time point 1 to time point ; Based on the energy efficiency dynamic evaluation results, balance the lighting power distribution, luminous flux maintenance and thermal management parameters through multi-objective optimization, and obtain the optimized lighting control strategy; Use the optimized lighting control strategy to adjust the LED lamp light curve, light source arrangement mode and sensing control threshold, optimize the energy consumption of LED lighting equipment, and obtain the LED lighting control implementation strategy.

2. The LED lighting efficiency optimization method of claim 1, wherein: The environmental and operation data analysis results include indoor and outdoor light intensity changes, temperature fluctuation range, and peak time period of people flow. The lighting demand prediction analysis results include predicted lighting intensity demand, color temperature adjustment demand, and lighting usage time. The real-time lighting adjustment strategy includes dynamic brightness adjustment parameters, color temperature change settings, and lighting switch time plan. The energy efficiency dynamic evaluation results include energy consumption trend analysis information, efficiency improvement point identification results, and potential energy saving area. The optimized lighting control strategy includes energy efficiency optimization configuration, illumination quality improvement measures, and user comfort improvement strategy. The LED lighting control implementation strategy includes implemented brightness setting, color temperature adjustment scheme, and LED lighting equipment switch logic.

3. The LED illumination efficiency optimization method of claim 1, wherein: Based on the lighting demand prediction analysis result, dynamically adjust the LED lighting driving current, PWM duty cycle and color temperature control parameters to obtain the specific steps of the real-time lighting adjustment strategy as follows: Based on the lighting demand prediction analysis result, compare the existing LED lighting configuration with the predicted demand, analyze the brightness, color temperature and switching state, identify the lighting parameters that need to be adjusted, and obtain the configuration and demand difference analysis result; Based on the configuration and demand difference analysis result, adjust the brightness and color temperature of the LED lighting device, calculate and set the switching time point, and optimize the lighting quality of the LED lighting device to obtain the adjusted lighting configuration; Based on the adjusted lighting configuration, optimize the energy efficiency of the LED lighting device by adjusting the configuration of the LED lighting device to obtain the real-time lighting adjustment strategy.

4. The LED illumination efficiency optimization method of claim 1, wherein: According to the real-time lighting adjustment strategy, combined with the LED lamp group energy consumption collection data, indoor temperature and humidity and illumination feedback data, the energy efficiency of the LED lighting device is evaluated in real time by using a hybrid dynamic Bayesian network to obtain the energy efficiency dynamic evaluation result. The specific steps are as follows: Based on the real-time lighting adjustment strategy, collect the real-time running data of the LED lighting device, including energy consumption, switching frequency, brightness level, and record the environmental variables, including indoor and outdoor temperature and humidity, to obtain the lighting operation and environmental state record; Based on the lighting operation and environmental state record, perform data analysis to analyze the correlation between the energy efficiency state of the LED lighting device and the environmental variables, identify the key environmental factors that cause the energy efficiency change, and obtain the energy efficiency correlation analysis result; Based on the energy efficiency correlation analysis result, use a hybrid dynamic Bayesian network to analyze the changes of the LED lighting device running data and environmental variables, and dynamically evaluate the energy efficiency state of the LED lighting device to obtain the energy efficiency dynamic evaluation result.

5. The LED illumination efficiency optimization method of claim 1, wherein: Based on the energy efficiency dynamic evaluation result, balance the lighting power distribution, luminous flux maintenance and thermal management parameters through multi-objective optimization to obtain the optimized lighting control strategy. The specific steps are as follows: Based on the energy efficiency dynamic evaluation result, analyze the difference between the current LED lighting device control parameters and the expected energy efficiency target, combine the brightness, color temperature and energy consumption factors, identify the priority and range of adjustment, and generate the lighting parameter adjustment analysis result; Based on the lighting parameter adjustment analysis result, develop a multi-objective optimization scheme to balance the energy efficiency, lighting quality and user comfort, and obtain the LED lighting device multi-objective optimization scheme; Based on the LED lighting device multi-objective optimization scheme, develop an optimized lighting control parameter setting, including brightness, color temperature and usage time, to optimize energy efficiency and user experience, and obtain the optimized lighting control strategy.

6. A LED lighting efficiency optimization system characterized by, The LED lighting efficiency optimization method according to any one of claims 1-5 is executed, and the system comprises: The data integration module collects and integrates real-time external environment data and historical LED lighting operation data, analyzes the relationship between the efficiency of the LED lighting device and the external environment, and obtains the efficiency environment relationship analysis result; The lighting trend analysis module analyzes the change trend of the lighting use mode and the environmental conditions based on the efficiency environment relationship analysis result to obtain the change trend analysis result; The lighting demand analysis module analyzes the lighting demand in the differentiated environmental conditions and time periods based on the change trend analysis result, identifies key variables, and obtains a lighting demand prediction result; The configuration adjustment module analyzes the difference between the current lighting configuration and the predicted demand based on the lighting demand prediction result, dynamically adjusts the LED lighting parameters to match the lighting demand, and obtains an adjusted lighting configuration; The energy efficiency analysis module collects operation data and environmental variables based on the adjusted lighting configuration, analyzes the energy efficiency status of the LED lighting device, and obtains an energy efficiency dynamic evaluation result; The optimization strategy module balances energy efficiency, lighting quality, and user comfort through a multi-objective optimization process based on the energy efficiency dynamic evaluation result, and obtains an optimized lighting control strategy; The control implementation module adjusts the LED lighting device settings based on the optimized lighting control strategy, matches the actual demand, performs performance evaluation and parameter tuning, and obtains an LED lighting control implementation strategy.

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