LED lighting efficiency optimization method and system

Abstract

The invention relates to the technical field of illumination control, in particular to an LED illumination efficiency optimization method and system, and the method comprises the following steps: recognizing an illumination use mode and a change trend of an environment condition based on real-time external environment data and historical LED illumination operation data, and obtaining an environment and operation data analysis result. According to the invention, prediction analysis is carried out on the LED illumination demand through the Gaussian process regression model, accurate prediction of the illumination demand under differentiated environmental conditions and time periods is realized, the responsiveness and adaptability of the illumination equipment are improved, the illumination control decision process is optimized by comprehensively considering the real-time external environment data and the historical illumination operation data, and the accuracy of the illumination control decision process is improved. The introduction of the hybrid dynamic Bayesian network enhances the real-time evaluation capability of the energy efficiency state of the LED lighting equipment, so that the energy efficiency management is more precise and efficient, and the use of a multi-objective optimization strategy provides a more comprehensive solution for balancing the energy efficiency, the illumination quality and the user comfort.

Description

Technical Field

[0001] The present invention relates to the technical field of lighting control, and in particular to a method and system for optimizing LED lighting efficiency. Background Art

[0002] The field of lighting control technology involves automated systems and methods for adjusting and controlling the brightness, color temperature and energy consumption of indoor and outdoor lighting. It aims to improve energy efficiency, improve 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 according to changes in natural light, space usage or specific activity requirements, thereby achieving energy savings and enhancing comfort.

[0003] Among them, the LED lighting efficiency optimization method focuses on improving the performance and energy efficiency of light-emitting diode LED lighting equipment. The purpose of this method is to reduce energy consumption, extend the life of lighting equipment, and ensure that the light quality meets the needs of users. Through thermal management, optical adjustment and intelligent control strategies, high energy and efficient light output are achieved, 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 inadequate energy utilization. For example, traditional methods cannot accurately adjust brightness and color temperature to meet lighting needs in different time periods, or cannot effectively utilize natural light, resulting in unnecessary energy consumption and reduced user experience. The lack of an effective real-time energy efficiency status evaluation 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, which in turn affects the energy efficiency and sustained performance of the overall lighting equipment. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a method and system for optimizing LED lighting efficiency.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: a method for optimizing LED lighting efficiency, comprising the following steps: S1: Based on real-time external environment data and historical LED lighting operation data, identify lighting usage patterns and environmental condition change trends, and obtain environmental and operation data analysis results; S2: Based on the analysis results of the environment and operation data, a Gaussian process regression model is used to predict the lighting demand under various weather conditions, time periods and spatial areas, and obtain a lighting demand prediction analysis result; S3: Based on the lighting demand forecast analysis result, dynamically adjust the LED lighting drive current, PWM duty cycle and color temperature control parameters to obtain a real-time lighting adjustment strategy; S4: According to the real-time lighting adjustment strategy, combined with the LED lamp group energy consumption collection data, indoor temperature, humidity and illumination feedback data, the hybrid dynamic Bayesian network is used to evaluate the energy efficiency of the LED lighting equipment in real time to obtain a dynamic energy efficiency evaluation result; S5: Based on the energy efficiency dynamic evaluation result, the lighting power distribution, luminous flux maintenance and thermal management parameters are balanced through multi-objective optimization to obtain an optimized lighting control solution; S6: Using the optimized lighting control solution, adjusting the LED lamp dimming curve, light source arrangement mode and sensing control threshold, optimizing the energy consumption of the LED lighting equipment, and obtaining an LED lighting control implementation plan.

[0007] The present invention has improvements in that the environmental and operational data analysis results include changes in indoor and outdoor light intensity, temperature fluctuation range, and peak traffic hours; the lighting demand forecast analysis results include predicted lighting intensity requirements, color temperature adjustment requirements, and lighting usage time; the real-time lighting adjustment strategy includes dynamic brightness adjustment parameters, color temperature change settings, and lighting switch 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 scheme includes energy efficiency optimization configuration, light quality improvement measures, and user comfort improvement strategies; the LED lighting control implementation plan includes implemented brightness settings, color temperature adjustment schemes, and LED lighting equipment switch logic.

[0008] The present invention is improved in that, according to the analysis results of the environment and operation data, a Gaussian process regression model is used to predict the lighting demand of various weather conditions, time periods and spatial areas, and the specific steps of obtaining the lighting demand prediction analysis results are as follows: S201: Based on the environmental and operational data analysis results, analyzing lighting requirements under differentiated environmental conditions and time periods, identifying key lighting requirement variables, including brightness requirements and lighting usage time, and obtaining lighting requirement key variable analysis results; S202: Analyze the lighting demand data under multiple time periods and environmental conditions based on the analysis results of the key variables of the lighting demand, identify the lighting demand patterns under different time periods and environmental conditions through cluster analysis, and obtain demand pattern recognition results; S203: Based on the demand pattern recognition result, a Gaussian process regression model is used to construct a lighting demand prediction model that matches differentiated environmental conditions and time periods, analyze and predict the lighting demand under differentiated environmental conditions and time periods, and obtain a lighting demand prediction analysis result.

[0009] The present invention improves that the Gaussian process regression model is based on formula I: ; Calculate the similarity between training data points and obtain the covariance matrix on the training points ,in, Kernel function, is the input point, As another input point, is the length scale parameter, is the distance adjustment factor, is the environmental adaptability adjustment coefficient, express and The distance between According to Formula II: ; Calculate the lighting demand at each prediction point and obtain the lighting demand prediction analysis results, where: is the input point, is the predicted value, is the covariance matrix at the training points, is the noise level, is the known training target value, is a matrix adjusted based on environmental factors, is the matrix weight coefficient, express The transpose of Represents a vector.

[0010] The present invention is improved in that, based on the lighting demand forecast analysis result, the LED lighting drive current, PWM duty cycle and color temperature control parameters are dynamically adjusted to obtain the specific steps of the real-time lighting adjustment strategy as follows: S301: Based on the lighting demand forecast analysis result, compare the existing LED lighting configuration with the forecast demand, analyze the brightness, color temperature and switch status, identify the lighting parameters that need to be adjusted, and obtain the configuration and demand difference analysis result; S302: Based on the analysis result of the difference between the configuration and the demand, adjust the brightness and color temperature of the LED lighting device, calculate and set the switching time point, optimize the lighting quality of the LED lighting device, and obtain the adjusted lighting configuration; S303: Based on the adjusted lighting configuration, the energy efficiency of the LED lighting device is optimized by adjusting the configuration of the LED lighting device to obtain a real-time lighting adjustment strategy.

[0011] The present invention is improved in that, 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, a hybrid dynamic Bayesian network is used to evaluate the energy efficiency of LED lighting equipment in real time, and the specific steps of obtaining the dynamic energy efficiency evaluation result are as follows: S401: Based on the real-time lighting adjustment strategy, collect real-time operation data of LED lighting equipment, including energy consumption, switching frequency, brightness level, and record environmental variables, including indoor and outdoor temperature and humidity, to obtain lighting operation and environmental status records; S402: Based on the lighting operation and environmental status records, data analysis is performed to analyze the correlation between the energy efficiency status of the LED lighting equipment and environmental variables, identify key environmental factors that cause energy efficiency changes, and obtain energy efficiency correlation analysis results; S403: Based on the energy efficiency correlation analysis result, a hybrid dynamic Bayesian network is used to analyze the changes in the operating data of the LED lighting equipment and the environmental variables, and the energy efficiency status of the LED lighting equipment is dynamically evaluated to obtain a dynamic energy efficiency evaluation result.

[0012] The present invention is improved in that the hybrid dynamic Bayesian network is based on the formula: ; Calculate the state probability at the next time point and generate the energy efficiency dynamic evaluation result, where: Indicates at a point in time Continuous energy efficiency data, Indicates at a point in time The discrete device operating status, represents the environmental factor parameter 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 discrete equipment operating status, Represents from time point 1 to time point A sequence of continuous energy efficiency data, Represents from time point 1 to time point A sequence of discrete device operating states.

[0013] The present invention is improved in that, based on the energy efficiency dynamic evaluation result, the specific steps of balancing lighting power distribution, luminous flux maintenance and thermal management parameters through multi-objective optimization to obtain an optimized lighting control solution are as follows: S501: 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; S502: Based on the lighting parameter adjustment analysis results, a multi-objective optimization scheme is formulated to balance energy efficiency, light quality and user comfort, thereby obtaining a multi-objective optimization scheme for LED lighting equipment; 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 scheme.

[0014] LED lighting efficiency optimization system, the system comprising: 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 use efficiency of LED lighting equipment and the external environment, and obtains the efficiency-environment relationship analysis results; The lighting trend analysis module analyzes the change trend of the lighting usage mode and the environmental conditions based on the efficiency-environment relationship analysis result to obtain a 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 results, identifies key variables, and obtains lighting demand prediction results; 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 the 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 equipment, and obtains a dynamic energy efficiency evaluation result; The optimization strategy module balances energy efficiency, light quality and user comfort through a multi-objective optimization process based on the energy efficiency dynamic evaluation result to obtain an optimized lighting control solution; Based on the optimized lighting control scheme, the control implementation module adjusts the LED lighting equipment settings to match the actual needs, performs performance evaluation and parameter tuning, and obtains the LED lighting control implementation plan.

[0015] Compared with the prior art, the advantages and positive effects of the present invention are: In the present invention, the Gaussian process regression model is used to predict and analyze the LED lighting demand, which realizes the accurate prediction of the lighting demand under differentiated environmental conditions and time periods, improves the responsiveness and adaptability of the lighting equipment, and optimizes the lighting control decision process by comprehensively considering the real-time external environmental data and historical lighting operation data, so that the lighting configuration is more in line with the actual needs, reduces energy waste and improves the user experience. The introduction of the hybrid dynamic Bayesian network enhances the real-time evaluation capability of the energy efficiency status of the LED lighting equipment, making the energy efficiency management more refined and efficient. The use of the multi-objective optimization strategy provides a more comprehensive solution in balancing energy efficiency, light quality and user comfort. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 A flow chart of the LED lighting efficiency optimization method proposed by the present invention; Figure 2 This is a schematic diagram of the detailed flow of step S1 in the LED lighting efficiency optimization method proposed in the present invention; Figure 3 This is a schematic diagram of the detailed process of step S2 in the LED lighting efficiency optimization method proposed in the present invention; Figure 4 This is a schematic diagram of the detailed flow of step S3 in the LED lighting efficiency optimization method proposed in the present invention; Figure 5 This is a schematic diagram of the detailed process of step S4 in the LED lighting efficiency optimization method proposed in the present invention; Figure 6 This is a schematic diagram of the detailed flow of step S5 in the LED lighting efficiency optimization method proposed in the present invention; Figure 7 This is a schematic diagram of the detailed process of step S6 in the LED lighting efficiency optimization method proposed in the present invention; Figure 8 The present invention proposes a module diagram of an LED lighting efficiency optimization system. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0018] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0019] Example

[0020] See also Figure 1 The present invention provides a technical solution: a method for optimizing LED lighting efficiency, comprising the following steps: S1: Based on real-time external environment data and historical LED lighting operation data, identify lighting usage patterns and environmental condition change trends, and obtain environmental and operation data analysis results; S2: Based on the analysis results of environmental and operational data, the Gaussian process regression model is used to predict the lighting demand under various weather conditions, time periods and spatial areas, and the lighting demand forecast analysis results are obtained; S3: Based on the lighting demand forecast 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; S4: Based on the real-time lighting adjustment strategy, combined with the LED light group energy consumption data, indoor temperature, humidity and illumination feedback data, the 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; S5: Based on the results of dynamic energy efficiency evaluation, the lighting power distribution, luminous flux maintenance and thermal management parameters are balanced through multi-objective optimization to obtain the optimized lighting control solution; S6: Using the optimized lighting control solution, adjusting the dimming curve of LED lamps, light source arrangement mode and sensing control threshold, optimizing the energy consumption of LED lighting equipment, and obtaining the LED lighting control implementation plan.

[0021] The results of environmental and operational data analysis include changes in indoor and outdoor light intensity, temperature fluctuation range, and peak traffic hours. The results of lighting demand forecast analysis include predicted lighting intensity requirements, color temperature adjustment requirements, and lighting usage time. Real-time lighting adjustment strategies include dynamic brightness adjustment parameters, color temperature change settings, and lighting switch time plans. The results of dynamic energy efficiency evaluation include energy consumption trend analysis information, efficiency improvement point identification results, and potential energy-saving areas. The optimized lighting control plan includes energy efficiency optimization configuration, light quality improvement measures, and user comfort improvement strategies. The LED lighting control implementation plan includes implemented brightness settings, color temperature adjustment plans, and LED lighting equipment switch logic.

[0022] See also Figure 2 , based on real-time external environment data and historical LED lighting operation data, identify lighting usage patterns and environmental condition change trends, and obtain the specific steps of environmental and operation data analysis results as follows: 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, data collection and integration are performed to evaluate the relationship between the use efficiency of LED lighting equipment and the external environment, and the specific process of generating data integration analysis results is as follows; 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 technology is used to perform detailed processing through Python's pandas library. SQL query statements are used to extract relevant data from the database. Pandas is used to merge data, process missing values, and eliminate outliers to ensure the accuracy and completeness of the data and generate data integration analysis results.

[0023] S102: Based on the data integration analysis results, through statistical analysis, identify the change trend of lighting usage patterns and environmental conditions, including light intensity and energy consumption fluctuations, and obtain the specific process of the change trend analysis results as follows; In sub-step S102, based on the data integration analysis results, statistical analysis is performed, and time series analysis technology is used. The autoregressive difference 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 to generate change trend analysis results.

[0024] S103: Based on the trend analysis results, the correlation between current and past lighting efficiency and environmental conditions is evaluated, the efficiency of the LED lighting equipment usage mode is analyzed, and the specific process of obtaining the environmental and operation data analysis results is as follows; In sub-step S103, based on the trend analysis results, the correlation between lighting efficiency and environmental conditions is evaluated. Linear regression analysis technology is used and implemented in R language. The lm() function is used to build 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, and generate environmental and operational data analysis results.

[0025] See also Figure 3 ,According to the results of environmental and operational data analysis, the Gaussian process regression model is used to predict the lighting demand in various weather conditions, time periods and spatial areas. The specific steps to obtain the lighting demand forecast analysis results are as follows: S201: Based on the environmental and operational data analysis results, analyze the lighting requirements under differentiated environmental conditions and time periods, identify key lighting requirement variables, including brightness requirements and lighting usage time, and obtain the specific process of the analysis results of the key lighting requirement variables as follows; In sub-step S201, based on the results of the environmental and operational data analysis, a detailed analysis of the lighting demand variables is performed. Data screening and feature engineering are performed through Python and pandas libraries. SQL statements are used to extract lighting-related time series data from large databases, such as brightness adjustment records, switching frequency, indoor and outdoor temperature, and light intensity. Pandas is used to clean and merge data, and statistical indicators such as mean and variance are calculated to identify key variables of brightness demand and lighting usage time, and generate analysis results of key variables of lighting demand.

[0026] S202: Based on the analysis results of the key variables of lighting demand, the lighting demand data under multiple time periods and environmental conditions are analyzed, and the lighting demand patterns under differentiated time periods and environmental conditions are identified through cluster analysis. The specific process of obtaining the demand pattern identification results is as follows; In sub-step S202, based on the analysis results of key variables of lighting demand, the lighting demand pattern is identified, and advanced clustering analysis technology is adopted. The K-Means clustering algorithm is executed using Python's scikit-learn library to perform data preprocessing, such as standardization and dimensionality reduction, to ensure the accuracy of clustering analysis. The lighting demand data in different time periods and environmental conditions are systematically classified, the lighting demand patterns in each time period and environmental conditions are identified, and the demand pattern recognition results are generated.

[0027] S203: Based on the demand pattern recognition result, a Gaussian process regression model is used to build a lighting demand prediction model that matches differentiated environmental conditions and time periods, analyze and predict the lighting demand under differentiated environmental conditions and time periods, and obtain the specific process of the lighting demand prediction analysis result as follows; 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, the model is configured and trained using Python's scikit-learn library, 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 differentiated environmental conditions and time periods is accurately analyzed and predicted, and the lighting demand prediction analysis results are generated.

[0028] Gaussian process regression model, according to Formula I: ; Calculate the similarity between training data points and obtain the covariance matrix on the training points ,in, Kernel function, used to calculate the input point and The similarity between is the input point, a data vector representing an environment or lighting condition, is another input point for The data vector to be compared, is the length scale parameter, which controls the sensitivity of the kernel function to the distance between input points. Distance adjustment factor, used to adjust the input point and The distance between The role of the kernel function is to help the model adjust its predictions according to the changes in the distance between points, and more accurately reflect the lighting needs under different spatial configurations. The 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 According to Formula II: ; Calculate the lighting demand at each prediction point and obtain the lighting demand prediction analysis results, where: is the input point, a data vector representing an environment or lighting condition, is the predicted value, that is, given the input Lighting demand forecast is the covariance matrix at the training points, reflecting the similarity between the training data points. is the noise level, which indicates the amount of noise considered in the prediction, is the known training target value, i.e., the historical lighting demand data, It is a matrix adjusted based on environmental factors, used to reflect the impact of environmental changes on lighting needs. is the matrix weight coefficient, determine the matrix importance in the forecast, numerically determined by data analysis methods to best reflect the impact of environmental factors on lighting requirements, express The transpose of Represents a vector, containing the input point and the kernel function value for each point in the training set.

[0029] The execution process of the improved formula is as follows: Setting the length scale parameter in the kernel function , and introduce new parameters and , in order to enhance the adaptability of the model to different environmental conditions, Adjust different distances The impact on the kernel function, Provides additional flexibility to accommodate various lighting needs by using kernel functions , calculate the input point and The similarity between them is calculated by using the kernel function to construct the covariance matrix , introducing the adjustment matrix into the prediction formula and weight coefficient , to consider the impact of environmental changes on lighting needs, Adjust according to environmental factors such as temperature, humidity, etc. Determined by data-driven methods, such as determining the optimal value through cross-validation, and then predicting the value Calculate and generate lighting demand forecast analysis results.

[0030] See also Figure 4, based on the lighting demand forecast analysis results, dynamically adjust the LED lighting drive current, PWM duty cycle and color temperature control parameters, and obtain the specific steps of the real-time lighting adjustment strategy as follows: 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 switch 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; In sub-step S301, based on the lighting demand forecast analysis results, the difference analysis between the LED lighting configuration and the demand is performed. Data analysis technology is used to perform data comparison through Python's pandas library. The current lighting configuration data, such as brightness level, color temperature setting, and switch 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 configuration and demand difference analysis results are generated.

[0031] S302: Based on the results of the configuration and demand difference analysis, the brightness and color temperature of the LED lighting device are adjusted, the switching time points are calculated and set, and the lighting quality of the LED lighting device is optimized. The specific process of obtaining the adjusted lighting configuration is as follows; In sub-step S302, based on the results of the configuration and demand difference analysis, the brightness and color temperature of the LED lighting equipment are adjusted. An intelligent control algorithm is used to interact with the API interface of the IoT platform through a Python script. An automated script is written to adjust the lighting parameters according to the difference analysis results, calculate the optimal switching time point to optimize the lighting quality, set logical judgments and loops to adjust the lighting equipment configuration in real time, ensure that the lighting system automatically adapts to the predicted demand, and generate the adjusted lighting configuration.

[0032] S303: Based on the adjusted lighting configuration, the configuration of the LED lighting device is adjusted to optimize the energy efficiency of the LED lighting device, and the specific process of obtaining the real-time lighting adjustment strategy is as follows; 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, energy consumption simulation and analysis are performed through MATLAB, and the energy consumption scenario of the lighting system is simulated using MATLAB's Simulink toolbox. The energy efficiency performance is evaluated according to the adjusted configuration, and optimization algorithms such as genetic algorithms or particle swarm optimization are executed to automatically adjust the lighting parameters to achieve optimal energy efficiency, and generate a real-time lighting adjustment strategy.

[0033] See also Figure 5 According to the real-time lighting adjustment strategy, combined with the LED lamp group energy consumption data, indoor temperature, humidity and illumination feedback data, the hybrid dynamic Bayesian network is used to evaluate the energy efficiency of LED lighting equipment in real time. The specific steps to obtain the dynamic evaluation results of energy efficiency are as follows: S401: Based on the real-time lighting adjustment strategy, the real-time operation data of the LED lighting equipment is collected, including energy consumption, switching frequency, brightness level, and environmental variables, including indoor and outdoor temperature and humidity, and the specific process of obtaining the lighting operation and environmental status record is as follows; In sub-step S401, based on the real-time lighting adjustment strategy, the Pandas library integrated in Python language is used to collect and process the real-time operation data of LED lighting equipment, including energy consumption data, the switching frequency of the equipment, and the measurement of brightness level, and synchronously record environmental variable data, such as indoor and outdoor temperature and humidity values. The data frame function of Pandas is used to store and organize multi-dimensional data, which is convenient for subsequent analysis operations and generates lighting operation and environmental status records.

[0034] S402: Based on the lighting operation and environmental status records, data analysis is performed to analyze the correlation between the energy efficiency status of the LED lighting equipment and environmental variables, identify the key environmental factors that cause energy efficiency changes, and obtain the specific process of energy efficiency correlation analysis results; In sub-step S402, based on the lighting operation and environmental status records, statistical analysis methods are used to implement correlation analysis through Python's SciPy library. The analysis aims to reveal the relationship between the energy efficiency status of LED lighting equipment and various environmental variables. By calculating the Spearman or Pearson correlation coefficient, key environmental factors that have a significant impact on LED lighting energy efficiency are identified. This analysis helps understand the changes in energy efficiency performance under different environmental conditions, thereby generating energy efficiency correlation analysis results.

[0035] S403: Based on the energy efficiency correlation analysis results, the hybrid dynamic Bayesian network is used to analyze the changes in the operation data of the LED lighting equipment and the environmental variables, and the energy efficiency status of the LED lighting equipment is dynamically evaluated. The specific process of obtaining the energy efficiency dynamic evaluation result is as follows; In sub-step S403, based on the results of energy efficiency correlation analysis, a hybrid dynamic Bayesian network is used to construct and operate the network in Python using the PGMPY library. This includes defining the network structure, parameter learning, and using appropriate inference algorithms to dynamically evaluate the energy efficiency status of LED lighting equipment. The network model will be continuously updated based on historical and real-time data to reflect the latest relationship between the device status and environmental variables, thereby generating dynamic energy efficiency evaluation results.

[0036] Hybrid dynamic Bayesian network, according to the formula: ; Calculate the state probability at the next time point and generate the energy efficiency dynamic evaluation result, where: Indicates at a point in time Continuous energy efficiency data, Indicates at a point in time The discrete device operating status, represents the 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 discrete equipment operating status, Represents a given point in time When continuous energy efficiency data, discrete equipment operating status and environmental factors are collected, the time point The conditional probability distribution of continuous energy efficiency data, Represents a given point in time When the discrete equipment operating status and environmental factors are The conditional probability distribution of the discrete device operating state, Represents from time point 1 to time point A sequence of continuous energy efficiency data, including energy efficiency data at each time point , reflects the history of the continuous energy efficiency status of LED lighting equipment from the past to the present moment, Represents from time point 1 to time point A sequence of discrete device operating states, including the device state at each point in time , showing the history of the equipment’s operating status from the past to the present moment.

[0037] The specific implementation process of the improved formula is as follows: Initialize the structure of the hybrid dynamic Bayesian network, including defining nodes and edges in the network. Nodes represent variables, including continuous energy efficiency data. , discrete device operating status , and newly added environmental factor parameters , the edges represent the dependencies between variables, and parameter learning is performed. Based on historical data sets, the conditional probability distribution in the network is learned and updated using methods such as maximum likelihood estimation or Bayesian estimation. and The conditional probability distribution expresses the probability of the state at the next time point given the current state and environmental factors. It uses real-time data for reasoning and according to the current state , and environmental factors , calculate the future state through the learned model and The probability distribution of the LED lighting device is obtained by using Bayesian reasoning and algorithms such as particle filtering or Kalman filtering to generate dynamic evaluation results on the energy efficiency status of the LED lighting device at the next time point based on the calculated probability distribution.

[0038] See also Figure 6 Based on the results of dynamic energy efficiency evaluation, the specific steps of balancing lighting power allocation, luminous flux maintenance and thermal management parameters through multi-objective optimization to obtain the optimized lighting control solution are as follows: S501: Based on the results of the dynamic energy efficiency evaluation, the difference between the current LED lighting equipment control parameters and the expected energy efficiency target is analyzed, and the priority and scope of adjustment are identified in combination with the brightness, color temperature and energy consumption factors. The specific process of generating the lighting parameter adjustment analysis results is as follows; In sub-step S501, based on the results of dynamic energy efficiency evaluation, a multivariate linear regression algorithm is used, and NumPy and SciPy libraries are used to perform parameter optimization and variance analysis, define the model structure, including setting the regression coefficient and intercept, and use the least squares method to calculate the optimal solution. The optimizer is set to the gradient descent method, and the mean square error is selected as the loss function to generate the lighting parameter adjustment analysis results. S502: Based on the lighting parameter adjustment analysis results, a multi-objective optimization plan is formulated to balance energy efficiency, light quality and user comfort. The specific process of obtaining the multi-objective optimization plan for LED lighting equipment is as follows; In sub-step S502, based on the analysis results of lighting parameter adjustment, a multi-objective optimization algorithm is used to implement the optimization process through Python's SciPy library, considering the three goals of energy efficiency, light quality and user comfort, setting the corresponding optimization objective function and constraints, and iteratively adjusting the parameters in the algorithm, such as mutation rate and crossover rate, to continuously adjust the parameters in the solution process to find the optimal solution, and generate a multi-objective optimization solution for LED lighting equipment; S503: Based on the multi-objective optimization scheme of 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 optimizing the lighting control scheme; In sub-step S503, based on the multi-objective optimization scheme of LED lighting equipment, a fuzzy logic control algorithm is adopted, and the Skfuzzy library is used to define fuzzy variables and rule sets, set fuzzy rules, and use the brightness error and color temperature error as input variables. The output variable is the control command adjustment amount. The maximum-minimum reasoning mechanism and the centroid defuzzification method are applied to optimize the LED lighting control parameters and generate an optimized lighting control scheme.

[0039] See also Figure 7 , using the optimized lighting control scheme, adjusting the LED lamp dimming curve, light source arrangement mode and induction control threshold, optimizing the energy consumption of LED lighting equipment, and obtaining the specific steps of the LED lighting control implementation plan are as follows: S601: Based on the optimized lighting control solution, the LED lighting equipment is adjusted, including the configuration of light brightness, color temperature selection and switch logic, to match the current demand and environmental conditions, and the specific process of instantly adjusting the lighting settings is obtained; In sub-step S601, based on the optimized lighting control solution, the light brightness, color temperature selection and switch logic configuration of the LED lighting equipment are performed, and Python scripts and RaspberryPi hardware interface control are used to adjust the brightness through PWM signals, adjust the color temperature through I2C communication protocol, and realize the switch operation through logic control to match the current needs and environmental conditions, and generate instant adjustment lighting settings; S602: Based on the real-time adjustment of lighting settings, the LED lighting equipment performance monitoring is implemented, the energy consumption and lighting effect evaluation is performed, and the current energy efficiency and performance status of the LED lighting equipment is determined. The specific process of obtaining the LED lighting equipment performance evaluation result is as follows; In sub-step S602, based on the instant adjustment of lighting settings, a real-time monitoring system is used to collect energy consumption and light data of LED lighting equipment using Arduino and sensors, and a time series analysis method is applied in combination with Python's Pandas and NumPy libraries to process and analyze the data, perform energy consumption and lighting effect evaluation, and generate LED lighting equipment efficiency evaluation results; S603: Based on the LED lighting equipment performance evaluation results, parameter adjustment and optimization are performed to optimize the operation of the LED lighting equipment, match energy efficiency and user experience, and obtain the specific process of the LED lighting control implementation plan; In sub-step S603, based on the LED lighting equipment efficiency evaluation results, the simulated annealing algorithm is used to adjust and optimize the parameters. The parameter configuration is evaluated by defining an energy function, setting the initial temperature and cooling rate, and iteratively searching for the optimal parameters. The Python script is used to optimize the operation of the LED lighting equipment and generate an LED lighting control implementation plan.

[0040] See also Figure 8 , LED lighting efficiency optimization system, the system includes: 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 use efficiency of LED lighting equipment and the external environment, and obtains the efficiency-environment relationship analysis results; The lighting trend analysis module analyzes the changing trends of lighting usage patterns and environmental conditions based on the efficiency-environment relationship analysis results, and obtains the changing trend analysis results; The lighting demand analysis module analyzes lighting demand under differentiated environmental conditions and time periods based on the trend analysis results, identifies key variables, and obtains lighting demand forecast results; The configuration adjustment module analyzes the difference between the current lighting configuration and the predicted demand based on the lighting demand prediction results, dynamically adjusts the LED lighting parameters to match the lighting demand, and obtains the adjusted lighting configuration; The energy efficiency analysis module collects operating data and environmental variables based on the adjusted lighting configuration, analyzes the energy efficiency status of LED lighting equipment, and obtains dynamic energy efficiency evaluation results; The optimization strategy module is based on the dynamic evaluation results of energy efficiency. Through a multi-objective optimization process, it balances energy efficiency, light quality and user comfort to obtain an optimized lighting control solution. The control implementation module optimizes the lighting control scheme, adjusts the LED lighting equipment settings, matches the actual needs, performs performance evaluation and parameter tuning, and obtains the LED lighting control implementation plan.

[0041] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A method for optimizing LED lighting efficiency, characterized in that: The following steps are involved: Based on real-time external environment data and historical LED lighting operation data, identify lighting usage patterns and environmental condition change trends, and obtain environmental and operation data analysis results; According to 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 obtain a lighting demand prediction analysis result; According to the analysis results of the environment and operation data, the Gaussian process regression model is used to predict the lighting demand under various weather conditions, time periods and spatial areas. The specific steps for obtaining the lighting demand prediction analysis results are as follows: Based on the environmental and operational data analysis results, analyzing lighting requirements under differentiated environmental conditions and time periods, identifying key lighting requirement variables, including brightness requirements and lighting usage time, and obtaining lighting requirement key variable analysis results; Based on the analysis results of the key variables of lighting demand, the lighting demand data under multiple time periods and environmental conditions are analyzed, and the lighting demand patterns under differentiated time periods and environmental conditions are identified through cluster analysis to obtain demand pattern recognition results; Based on the demand pattern recognition results, a Gaussian process regression model is used to build a lighting demand prediction model that matches differentiated environmental conditions and time periods, analyze and predict the lighting demand under differentiated environmental conditions and time periods, and obtain a lighting demand prediction analysis result; Based on the lighting demand forecast analysis results, dynamically adjust LED lighting drive current, PWM duty cycle and color temperature control parameters to obtain a real-time lighting adjustment strategy; 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 hybrid dynamic Bayesian network is used to evaluate the energy efficiency of the LED lighting equipment in real time to obtain the dynamic energy efficiency evaluation result; Based on the energy efficiency dynamic evaluation results, the lighting power distribution, luminous flux maintenance and thermal management parameters are balanced through multi-objective optimization to obtain an optimized lighting control solution; By using the optimized lighting control scheme, the dimming curve of LED lamps, the light source arrangement mode and the sensing control threshold are adjusted to optimize the energy consumption of LED lighting equipment and obtain an LED lighting control implementation plan.

2. The LED lighting efficiency optimization method according to claim 1, characterized in that: The environmental and operational data analysis results include changes in indoor and outdoor light intensity, temperature fluctuation range, and peak traffic hours; the lighting demand forecast analysis results include predicted lighting intensity requirements, color temperature adjustment requirements, and lighting usage time; the real-time lighting adjustment strategy includes dynamic brightness adjustment parameters, color temperature change settings, and lighting switch 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 solution includes energy efficiency optimization configuration, light quality improvement measures, and user comfort improvement strategies; the LED lighting control implementation plan includes implemented brightness settings, color temperature adjustment solutions, and LED lighting equipment switch logic.

3. The LED lighting efficiency optimization method according to claim 1, characterized in that: The Gaussian process regression model is based on Formula I: ； Calculate the similarity between training data points and obtain the covariance matrix on the training points ,in, Kernel function, is the input point, As another input point, is the length scale parameter, is the distance adjustment factor, is the environmental adaptability adjustment coefficient, express and The distance between According to Formula II: ； Calculate the lighting demand at each prediction point and obtain the lighting demand prediction analysis results, where: is the input point, is the predicted value, is the covariance matrix at the training points, is the noise level, is the known training target value, is a matrix adjusted based on environmental factors, is the matrix weight coefficient, express The transpose of Represents a vector.

4. The LED lighting efficiency optimization method according to claim 1, characterized in that: Based on the lighting demand forecast analysis results, the specific steps of dynamically adjusting the LED lighting drive current, PWM duty cycle and color temperature control parameters to obtain a real-time lighting adjustment strategy are as follows: Based on the lighting demand forecast analysis results, compare the existing LED lighting configuration with the forecast demand, analyze the brightness, color temperature and switch status, identify the lighting parameters that need to be adjusted, and obtain the configuration and demand difference analysis results; Based on the analysis results of the differences between the configuration and the requirements, the brightness and color temperature of the LED lighting device are adjusted, the switching time points are calculated and set, the lighting quality of the LED lighting device is optimized, and the adjusted lighting configuration is obtained; Based on the adjusted lighting configuration, the energy efficiency of the LED lighting device is optimized by adjusting the configuration of the LED lighting device, thereby obtaining a real-time lighting adjustment strategy.

5. The LED lighting efficiency optimization method according to claim 1, characterized in that: According to the real-time lighting adjustment strategy, combined with the LED light group energy consumption data, indoor temperature, humidity and illumination feedback data, the hybrid dynamic Bayesian network is used to evaluate the energy efficiency of LED lighting equipment in real time. The specific steps for obtaining the dynamic evaluation results of energy efficiency are as follows: Based on the real-time lighting adjustment strategy, real-time operation data of LED lighting equipment is collected, including energy consumption, switching frequency, brightness level, and environmental variables, including indoor and outdoor temperature and humidity, to obtain a record of lighting operation and environmental status; Based on the lighting operation and environmental status records, data analysis is performed to analyze the correlation between the energy efficiency status of the LED lighting equipment and environmental variables, identify key environmental factors that cause energy efficiency changes, and obtain energy efficiency correlation analysis results; Based on the energy efficiency correlation analysis results, a hybrid dynamic Bayesian network is used to analyze the changes in the operating data of the LED lighting equipment and the environmental variables, and the energy efficiency status of the LED lighting equipment is dynamically evaluated to obtain the energy efficiency dynamic evaluation results.

6. The LED lighting efficiency optimization method according to claim 5, characterized in that: The hybrid dynamic Bayesian network is based on the formula: ； Calculate the state probability at the next time point and generate the energy efficiency dynamic evaluation result, where: Indicates at a point in time Continuous energy efficiency data, Indicates at a point in time The discrete device operating status, represents the environmental factor parameter 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 discrete equipment operating status, Represents from time point 1 to time point A sequence of continuous energy efficiency data, Represents from time point 1 to time point A sequence of discrete device operating states.

7. The LED lighting efficiency optimization method according to claim 1, characterized in that: Based on the dynamic evaluation results of energy efficiency, the lighting power distribution, luminous flux maintenance and thermal management parameters are balanced through multi-objective optimization to obtain the optimized lighting control solution. The specific steps are as follows: Based on the energy efficiency dynamic evaluation results, the difference between the current LED lighting equipment control parameters and the expected energy efficiency target is analyzed, and the priority and scope of adjustment are identified in combination with brightness, color temperature and energy consumption factors, so as to generate lighting parameter adjustment analysis results; Based on the lighting parameter adjustment analysis results, a multi-objective optimization scheme is formulated to balance energy efficiency, light quality and user comfort, and a multi-objective optimization scheme for LED lighting equipment is obtained; Based on the multi-objective optimization scheme of LED lighting equipment, optimized lighting control parameter settings are formulated, including brightness, color temperature and usage time, energy efficiency and user experience are optimized, and an optimized lighting control scheme is obtained. 8.LED lighting efficiency optimization system, characterized in that, According to the LED lighting efficiency optimization method according to any one of claims 1 to 7, the system comprises: 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 use efficiency of LED lighting equipment and the external environment, and obtains the efficiency-environment relationship analysis results; The lighting trend analysis module analyzes the change trend of the lighting usage mode and the environmental conditions based on the efficiency-environment relationship analysis result to obtain a 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 results, identifies key variables, and obtains lighting demand prediction results; 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 the 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 equipment, and obtains a dynamic energy efficiency evaluation result; The optimization strategy module balances energy efficiency, light quality and user comfort through a multi-objective optimization process based on the energy efficiency dynamic evaluation result to obtain an optimized lighting control solution; Based on the optimized lighting control scheme, the control implementation module adjusts the LED lighting equipment settings to match the actual needs, performs performance evaluation and parameter tuning, and obtains the LED lighting control implementation plan.

Images