LED lamp energy efficiency improving system

Abstract

The invention relates to the technical field of electronic control, in particular to an LED lamp energy efficiency improving system which comprises a light color stability analysis module, a current adjusting strategy module, a use mode analysis module, an energy-saving dimming strategy module, an execution effect monitoring module and a strategy adjusting feedback module. According to the invention, through the innovation of the light color stability analysis and the energy-saving dimming strategy, the performance of the LED lamp is obviously optimized, and the light color stability analysis module analyzes the temperature and current data during operation, accurately adjusts the current intensity to adapt to the temperature change, effectively controls the light color shift, guarantees the light color stability, and improves the light color stability. Uniform light color performance of the LED lamp in various environments is ensured, adaptability and user experience of the lamp are improved, the energy-saving dimming strategy module automatically adjusts brightness according to use habits of a user, energy consumption is reduced while actual requirements are met, the service life of the lamp is prolonged, maintenance cost is reduced, intelligent dimming is achieved, personalized service is embodied, and the intelligent LED lamp is suitable for popularization and application. And energy efficiency maximization is promoted.

Description

Technical Field

[0001] The present invention relates to the field of electronic control technology, and in particular to a system for improving the energy efficiency of LED lamps. Background Art

[0002] In the field of electronic control technology, we focus on improving the energy efficiency and service life of light sources while reducing energy consumption. This field involves precise current control technology, thermal management solutions and light efficiency optimization strategies to ensure that LED lamps can perform optimally in different application scenarios. The electronic control system maximizes the luminous efficiency of LEDs by finely adjusting the power supply parameters, while protecting LEDs from potential damage such as overcurrent and overheating, thereby extending their service life.

[0003] Among them, the LED lamp energy efficiency improvement system aims to optimize the energy efficiency and performance of LED lamps through electronic control technology. The core purpose of the system is to reduce energy consumption while providing equal or higher lighting levels to support environmental protection and energy conservation. By improving the energy efficiency of LED lamps, the system not only reduces power consumption, but also reduces performance degradation caused by overheating, further extending the service life of LED lamps. Energy efficiency improvement also means reducing maintenance costs and replacement frequency, bringing double benefits to users and the environment.

[0004] Traditional LED lamp energy efficiency improvement systems have obvious deficiencies in dynamic environmental adaptability and personalized energy efficiency management. In a changing usage environment, traditional LED lamps cannot effectively adjust their light color stability and brightness, resulting in reduced light efficiency, impaired user experience, and even shortened service life due to excessive current consumption. LED lamps that cannot automatically adjust light color and brightness according to changes in ambient temperature are prone to color temperature deviations after long-term use, increasing user dissatisfaction with the product and exacerbating energy waste. The lack of in-depth analysis and prediction of user usage habits makes traditional lamps limited in effect in energy-saving and dimming, and cannot be accurately adjusted according to actual needs, further limiting their energy efficiency improvement potential. Summary of the invention

[0005] The present application provides an LED lamp energy efficiency improvement system to solve the obvious deficiencies of traditional LED lamp energy efficiency improvement systems in terms of dynamic environmental adaptability and personalized energy efficiency management. In a changing usage environment, traditional LED lamps cannot effectively adjust their light color stability and brightness, resulting in reduced light efficiency, impaired user experience, and even shortened service life due to excessive current consumption. LED lamps that cannot automatically adjust light color and brightness according to changes in ambient temperature are prone to color temperature deviation after long-term use, increasing user dissatisfaction with the product and aggravating energy waste. The lack of in-depth analysis and prediction of user usage habits makes traditional lamps limited in effect in energy-saving and dimming, and cannot be accurately adjusted according to actual needs, further limiting their energy efficiency improvement potential.

[0006] In view of the above problems, the present application provides a system for improving energy efficiency of LED lamps.

[0007] The present application provides a system for improving energy efficiency of LED lamps, wherein the system comprises: The light color stability analysis module collects the temperature and current intensity data of LED lamps during operation, conducts direct correlation analysis on the impact of temperature and current intensity on light color, screens the light color change trend evaluation data, analyzes the relationship between temperature, current intensity and light color drift, determines the key influencing factors of light color change, and obtains the light color change trend evaluation results; The current regulation strategy module adjusts the current intensity based on the light color change trend evaluation result, calculates the current adjustment gradient for the warm light color state, reduces the current intensity, and increases the current intensity for the cold light color state, thereby obtaining an adjustment current parameter table; The usage mode analysis module collects the switch status and brightness adjustment records of the intelligent driving circuit based on the adjustment current parameter table, analyzes historical data, screens the frequency of use, switch cycle, and brightness adjustment behavior of the lamp, analyzes the distribution law of the usage mode, extracts the user operation preference, and obtains the user habit analysis result; The energy-saving dimming strategy module adjusts the brightness level of the LED lamp dimming module based on the user habit analysis results, analyzes the brightness adjustment range for the time period when the user's operation frequency is reduced, calculates the energy-saving dimming parameters, matches the user's operation preference, determines the energy-saving dimming parameter table, records the brightness adjustment details, and constructs energy-saving dimming rules; The execution effect monitoring module monitors the energy consumption and light color stability data after the implementation of the energy-saving dimming rules, analyzes the impact of different dimming strategies on energy consumption changes, records light color changes, collects monitoring data, calculates energy consumption change trends, screens light color stability fluctuation ranges, and obtains execution effect monitoring results; Based on the execution effect monitoring results, the strategy adjustment feedback module analyzes the current strategy data, compares the changes in energy consumption and light color stability before and after the adjustment, filters abnormal data, determines the adaptability of energy-saving dimming rules, calculates the optimization adjustment coefficient, adjusts the dimming strategy of the intelligent drive control module, records the optimization parameters, and obtains the strategy optimization plan.

[0008] Preferably, the light color change trend evaluation results include parameters related to temperature rise and warm light color, parameters related to current intensity change and cool light color, and light color change trend indicators; the current adjustment parameter table includes current adjustment target value, current adjustment amplitude, and current adjustment strategy execution time point; the user habit analysis results include usage time period, preferred brightness level, and user habit matching score; the energy-saving dimming rules include automatic lamp brightness adjustment range, preset brightness adjustment schedule, and energy-saving target setting value; the execution effect monitoring results include energy saving rate, light color stability maintenance index, and user satisfaction score; the strategy optimization plan includes adjusted current control strategy parameters, lamp brightness adjustment optimization strategy, and expected energy efficiency optimization percentage.

[0009] Preferably, the light color stability analysis module includes: The temperature and current monitoring submodule collects the temperature and current intensity data of the LED lamp during operation. If the temperature and current intensity exceed the preset threshold, they are adjusted to the standard range to generate temperature and current adjustment data. The light color deviation analysis submodule uses a linear regression analysis method based on the temperature and current adjustment data to analyze the relationship between temperature increase and warm light color, and between current intensity change and cold light color. If there is a correlation, the correlation model between temperature and warm light color, and between current intensity and cold light color is established to obtain the light color deviation correlation analysis result. The trend assessment submodule is based on the light color deviation correlation analysis result, and by comparing the light color changes under differentiated conditions, if the analysis shows a consistent trend toward warming or cooling, the trend is quantitatively assessed to obtain a light color change trend assessment result.

[0010] Preferably, the linear regression analysis method is according to the formula: ; in, is the light color deviation value, is the data after temperature adjustment, is the data after current adjustment, is the ambient brightness, For the duration of use, is the intercept term, is the slope parameter, is the interaction term between ambient brightness and usage duration, is the error term.

[0011] Preferably, the energy-saving dimming strategy module includes: The brightness preference analysis submodule uses the user habit analysis results to sort out the brightness preferences within the time period, identifies the low-frequency activity time period and the corresponding brightness preference through the correlation analysis between the user activity frequency and the brightness adjustment record, sets the threshold and target of the brightness adjustment, and obtains the brightness preference setting; The brightness adjustment execution submodule adopts a dynamic adjustment strategy based on the brightness preference setting, automatically adjusts the brightness of the lamp by monitoring the matching degree between the current time and the set brightness preference, and sets the adjustment range and speed according to the user preference and real-time situation, and generates dimming execution details; The adjustment log recording submodule records the data of each dimming operation based on the dimming execution details, including the dimming time, the values ​​before and after the brightness adjustment, and the dimming reason, tracks the brightness adjustment effect, and generates energy-saving dimming rules.

[0012] Preferably, the execution effect monitoring module includes: The energy consumption data analysis submodule records the energy consumption data after the implementation of the energy-saving dimming rule. If the data shows that the energy consumption is reduced, it is marked as the best energy-saving effect. By tracking the energy consumption change trend and comparing the energy consumption data difference before and after energy saving, the energy consumption change analysis result is obtained; The light color stability analysis submodule applies variance analysis method based on the energy consumption change analysis result to record the light color change. If the light color remains within the user preference range, the light color stability is determined to be good. By real-time tracking of the light color stability, the influence of the dimming rule on the light color stability is evaluated to obtain the light color stability evaluation result. The monitoring data summary submodule summarizes the monitoring data of the lamps based on the light color stability evaluation results, integrates the energy consumption and light color stability data, and if the energy consumption and light color stability data meet the expected results, it is marked as having excellent execution effect and generates an execution effect monitoring result.

[0013] Preferably, the variance analysis method is according to the formula: ; in, is the adjusted variance, is the number of color temperature data points collected within the user's preferred range, is the color temperature value of each data point, is the average color temperature of the data points, is the weight coefficient for each color temperature data point, is the current ambient temperature, is the optimal ambient temperature, is the current humidity, For optimal humidity, is the current light intensity, For the optimal light intensity, is the weight coefficient of ambient temperature, humidity and light intensity.

[0014] Preferably, the strategy adjustment feedback module includes: The effect analysis submodule uses the execution effect monitoring results to analyze energy consumption data and light color stability, and by comparing the energy consumption difference and light color change before and after dimming, identifies the impact of energy-saving measures and the effectiveness of light color control, and obtains effect comparison and adjustment needs; The parameter adjustment record submodule analyzes the parameter changes required for the adjustment strategy based on the effectiveness comparison and adjustment requirements, records the reasons, goals and expected impacts of each parameter adjustment, refines the selection and adjustment range of the adjustment parameters, and generates a parameter adjustment log; The optimization strategy recording submodule adjusts the parameter log, summarizes and analyzes the adjustment behavior and the expected optimization of energy saving effect and light color control, and comprehensively evaluates the potential optimization direction of the adjusted strategy, including summarizing the effect of the adjustment measures, pointing out the content of the optimization measures, and generating an overview of the comprehensive optimization plan.

[0015] One or more technical solutions provided in this application have at least the following technical effects or advantages: Through the innovative application of the light color stability analysis module and the energy-saving dimming strategy module, the performance of LED lamps is significantly optimized. The light color stability analysis module collects the temperature and current intensity data during operation, and accurately adjusts the current intensity to adapt to temperature changes by correlating the data, effectively controlling the light color deviation and maintaining the stability of the light color. This analysis and adjustment mechanism enables LED lamps to maintain consistent light color performance under different environmental conditions, avoiding color temperature deviation caused by temperature changes or current instability, and improving the adaptability and user experience of the lamps. The energy-saving dimming strategy module automatically adjusts the brightness according to the user's usage habits, which not only meets the actual needs of users and reduces unnecessary energy consumption, but also further extends the life of LED lamps and reduces maintenance costs by finely controlling the brightness. Intelligent dimming based on user behavior data not only reflects personalized services, but also promotes maximum energy efficiency.

[0016] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The module diagram of the LED lamp energy efficiency improvement system proposed by the present invention; Figure 2 The system framework diagram of the LED lamp energy efficiency improvement system proposed by the present invention; Figure 3 A schematic diagram of the specific process of the light color stability analysis module of the LED lamp energy efficiency improvement system proposed by the present invention; Figure 4 A specific flow chart of the current regulation strategy module of the LED lamp energy efficiency improvement system proposed by the present invention; Figure 5 A specific flow chart of the usage mode analysis module of the LED lamp energy efficiency improvement system proposed by the present invention; Figure 6 A specific flow chart of the energy-saving dimming strategy module of the LED lamp energy efficiency improvement system proposed by the present invention; Figure 7 A specific flow chart of the LED lamp energy efficiency improvement system execution effect monitoring module proposed by the present invention; Figure 8 The present invention provides a specific flow chart of the strategy adjustment feedback module of the LED lamp energy efficiency improvement system. DETAILED DESCRIPTION

[0018] The present application provides an LED lamp energy efficiency improvement system to solve the obvious deficiencies of traditional LED lamp energy efficiency improvement systems in terms of dynamic environmental adaptability and personalized energy efficiency management. In a changing usage environment, traditional LED lamps cannot effectively adjust their light color stability and brightness, resulting in reduced light efficiency, impaired user experience, and even shortened service life due to excessive current consumption. LED lamps that cannot automatically adjust light color and brightness according to changes in ambient temperature are prone to color temperature deviation after long-term use, increasing user dissatisfaction with the product and aggravating energy waste. The lack of in-depth analysis and prediction of user usage habits makes traditional lamps limited in effect in energy-saving and dimming, and cannot be accurately adjusted according to actual needs, further limiting their energy efficiency improvement potential.

[0019] Application Overview The traditional LED lamp energy efficiency improvement system in the prior art has obvious deficiencies in dynamic environmental adaptability and personalized energy efficiency management. In a changing usage environment, traditional LED lamps cannot effectively adjust their light color stability and brightness, resulting in reduced light efficiency, impaired user experience, and even shortened service life due to excessive current consumption. LED lamps that cannot automatically adjust light color and brightness according to changes in ambient temperature are prone to color temperature deviation after long-term use, increasing user dissatisfaction with the product and aggravating energy waste. The lack of in-depth analysis and prediction of user usage habits makes traditional lamps limited in energy-saving and dimming effects, and cannot be accurately adjusted according to actual needs, further limiting their energy efficiency improvement potential.

[0020] In response to the above technical problems, the overall idea of ​​the technical solution provided by this application is as follows: like Figure 1 , 2 As shown, the present application provides a system for improving energy efficiency of LED lamps, wherein the system comprises: The light color stability analysis module collects the temperature and current intensity data of LED lamps during operation, conducts direct correlation analysis on the impact of temperature and current intensity on light color, screens the light color change trend evaluation data, analyzes the relationship between temperature, current intensity and light color drift, determines the key influencing factors of light color change, and obtains the light color change trend evaluation results; The current regulation strategy module adjusts the current intensity based on the evaluation results of the light color change trend. For the warm light color state, the current adjustment gradient is calculated to reduce the current intensity. For the cold light color state, the current intensity is increased to obtain the adjustment current parameter table. The usage mode analysis module collects the switch status and brightness adjustment records of the intelligent drive circuit based on the adjustment current parameter table, analyzes historical data, screens the frequency of lamp use, switch cycle, and brightness adjustment behavior, analyzes the distribution of usage modes, extracts user operation preferences, and obtains user habit analysis results; The energy-saving dimming strategy module adjusts the brightness level of the LED lamp dimming module based on the user habit analysis results, analyzes the brightness adjustment range for the time period when the user's operation frequency is reduced, calculates the energy-saving dimming parameters, matches the user's operation preference, determines the energy-saving dimming parameter table, records the brightness adjustment details, and builds energy-saving dimming rules; The execution effect monitoring module monitors the energy consumption and light color stability data after the implementation of energy-saving dimming rules, analyzes the impact of different dimming strategies on energy consumption changes, records light color changes, collects monitoring data, calculates energy consumption change trends, screens light color stability fluctuation ranges, and obtains execution effect monitoring results; Based on the execution effect monitoring results, the strategy adjustment feedback module analyzes the current strategy data, compares the changes in energy consumption and light color stability before and after the adjustment, filters out abnormal data, determines the adaptability of energy-saving dimming rules, calculates the optimization adjustment coefficient, adjusts the dimming strategy of the intelligent drive control module, records the optimization parameters, and obtains the strategy optimization plan.

[0021] The evaluation results of light color change trend include parameters related to temperature rise and warm light color, parameters related to current intensity change and cool light color, and light color change trend indicators. The table of current adjustment parameters includes current adjustment target value, current adjustment amplitude, and current adjustment strategy execution time point. The results of user habit analysis include usage time period, preferred brightness level, and user habit matching score. The energy-saving dimming rules include automatic brightness adjustment range of lamps, preset brightness adjustment schedule, and energy-saving target setting value. The results of execution effect monitoring include energy saving rate, light color stability maintenance index, and user satisfaction score. The strategy optimization plan includes adjusted current control strategy parameters, lamp brightness adjustment optimization strategy, and expected energy efficiency optimization percentage.

[0022] Specifically, if Figure 2 , 3 As shown, the light color stability analysis module includes: The temperature and current monitoring submodule collects the temperature and current intensity data of the LED lamp when it is running. If the temperature and current intensity exceed the preset threshold, they are adjusted to within the standard range. The execution process of generating temperature and current adjustment data is as follows; The temperature and current monitoring submodule is based on sensor data and uses a moving average filtering algorithm to smooth the temperature and current intensity data. The Python numpy library is used to calculate the moving average of a sliding window of 5, and the temperature and current intensity collected at each data point are smoothed to reduce the impact of instantaneous noise. The threshold judgment logic is used to set the temperature threshold to 30°C and the current threshold to 2A. The conditional judgment statement is used to compare the smoothed data with the threshold. If the data exceeds the threshold, the control circuit that adjusts the current to power the LED lamp can reduce or increase the current to adjust the temperature and current intensity to within the standard range, and generate temperature and current adjustment data.

[0023] The light color deviation analysis submodule uses linear regression analysis method based on temperature and current adjustment data to analyze the relationship between temperature increase and warm light color, and current intensity change and cold light color. If there is a correlation, the correlation model between temperature and warm light color, and current intensity and cold light color is established respectively. The execution process of obtaining the light color deviation correlation analysis result is as follows; The light color deviation analysis submodule is based on the temperature and current adjustment data. The linear regression analysis method is used to analyze the relationship between temperature increase and warm light color, and between current intensity change and cold light color. The scikit-learn library is used to build a linear regression model. The input variables are the temperature and current adjusted data, and the output variable is the light color deviation value. By fitting the data of temperature and warm light color, and current intensity and cold light color, the association model between temperature and warm light color, and current intensity and cold light color is established, and the light color deviation association analysis results are obtained.

[0024] The linear regression analysis method is based on the formula: ; in, is the light color deviation value, is the data after temperature adjustment, is the data after current adjustment, is the ambient brightness, For the duration of use, is the intercept term, is the slope parameter, is the interaction term between ambient brightness and usage duration, is the error term.

[0025] The execution process is as follows; The adjustment data of temperature, current, ambient brightness and usage time are collected to ensure the accuracy and reliability of the data. The linear regression model is constructed using the scikit-learn library. The input variables are the temperature, current adjusted data, ambient brightness and usage time, and the output variable is the light color deviation value. By fitting the data, a more complex and refined association model is established. Two new parameters, ambient brightness and usage time, and interaction terms are added to the model to improve the accuracy of the prediction. and The coefficient value is determined by fitting the data during the model training process to ensure that the model can more accurately reflect the complex relationship between temperature increase and warm light color, and between current intensity change and cool light color, including the interaction between factors and ambient brightness and usage time, so as to obtain a more accurate correlation analysis result of light color deviation.

[0026] The trend assessment submodule is based on the light color deviation correlation analysis results. By comparing the light color changes under differentiated conditions, if the analysis shows a consistent trend towards warming or cooling, the trend is quantitatively assessed to obtain the light color change trend assessment results. The execution process is as follows; The trend assessment submodule is based on the results of the light color deviation correlation analysis. It adopts the time series analysis method and uses the statsmodels library to construct the autoregressive integrated moving average (ARIMA) model. The light color deviation correlation analysis results are input as time series data, and the model parameters are set to (2, 1, 2), which represent the autoregressive term, the difference order, and the order of the moving average term, respectively. The trend of light color change is predicted through the model. If the analysis shows a consistent trend towards warming or cooling, the trend is quantitatively evaluated and the light color change trend assessment results are obtained.

[0027] Specifically, if Figure 2 , 4 As shown, the current regulation strategy module includes: The light color trend recognition submodule analyzes the light color change trend assessment results, identifies the warm and cold light color trends, and determines the preliminary direction of current adjustment through trend change judgment. The execution process of trend recognition guidance is as follows; The light color trend recognition submodule is based on the light color change trend assessment results, adopts the decision tree algorithm, uses the Scikit-learn library to implement model training, defines the decision tree parameters, including setting the maximum depth max_depth to 5, the minimum sample split min_samples_split to 10, and uses information gain as the node splitting criterion. The decision tree analyzes the association between temperature, current intensity data and light color shift trends, identifies the trend of warmer or cooler light colors based on the analysis results, and generates trend recognition guidance.

[0028] The current adjustment strategy submodule receives the trend recognition guidance and gradually adjusts the current intensity, including adjusting the current size to match the light color deviation, decreasing the current for warmer trends and increasing it for colder trends. Referring to the changes in light color performance and current intensity, the execution process of the adjustment strategy solution is as follows; The current adjustment strategy submodule receives trend recognition guidance, adopts a linear regression model, uses the NumPy library to perform linear regression analysis, defines model parameters, including weight initialization to 0, learning rate setting to 0.01, and number of iterations set to 1000. The linear regression model is used to predict the linear relationship between current intensity and light color change. The current intensity is adjusted according to the prediction results, decreasing or increasing to match the light color deviation, and an adjustment strategy plan is generated.

[0029] The parameter recording and updating submodule records the current adjustment parameters according to the adjustment strategy scheme, marks each adjustment operation, assigns an identification code to each operation, records the operation date and time, monitors the impact of current adjustment on performance, analyzes the impact of current value changes on the operating status of the equipment, and generates the adjustment current parameter table. The execution process is as follows; The parameter recording and updating submodule uses a database management system (specifically SQLite) according to the adjustment strategy plan, executes SQL commands to insert data, and sets the parameter recording table, including setting the fields as the operation identification code INTEGERPRIMARYKEY, operation date DATE, time TIME, and current adjustment value FLOAT. The INSERT INTO statement is used to record the detailed information of each adjustment operation, including the operation identification code, operation date and time, and the specific value of the current adjustment. The data is summarized through the query statement SELECT FROM to generate the adjustment current parameter table.

[0030] Specifically, if Figure 2 , 5 As shown, the usage pattern analysis module includes: The state brightness recording submodule collects the switch state and brightness adjustment history records of LED lamps based on the adjustment current parameter table, analyzes the usage frequency and brightness changes of LED lamps, and obtains the execution process of the usage state recording results as follows; The status brightness recording submodule is based on the adjustment current parameter table and uses the event-triggered recording method to collect the switch status and brightness adjustment history records of LED lamps. The Python script is used to monitor the hardware interrupt signal. Whenever the status of the LED lamp changes, the current time, status (on / off), and brightness value are automatically recorded in the local database. The database uses SQLite because it is lightweight and easy to integrate into a variety of applications. By executing SQL query statements, the three fields of timestamp, status, and brightness are filtered, and the recorded data is formatted in JSON format for subsequent processing. The usage frequency and brightness changes of LED lamps are analyzed to obtain the usage status record results.

[0031] The usage habit analysis submodule identifies the user's light switching habits and brightness adjustment preferences based on the usage status record results, and determines the usage period and brightness preference through statistical methods. The execution process of obtaining the habit preference analysis results is as follows; The usage habit analysis submodule uses the k-means clustering algorithm to identify users' light switching habits and brightness adjustment preferences based on the usage status record results. The scikit-learn library is used to perform cluster analysis. First, the usage status record results are preprocessed, including normalized brightness values ​​and encoded switch states (on = 1, off = 0). Then the number of clusters is set to 2, representing two key usage habits. The number of algorithm iterations is set to 300 to ensure convergence. By calculating the Euclidean distance between each record and the cluster center, the record is assigned to the nearest cluster center, thereby determining the usage period and brightness preference, and obtaining the habit preference analysis results.

[0032] The habit matching analysis submodule performs matching analysis between time periods and brightness levels based on the habit preference analysis results, analyzes the relationship between the user's preferred brightness level and the usage period, and obtains the user habit analysis results. The execution process is as follows; The habit matching analysis submodule uses a decision tree algorithm to perform matching analysis between time periods and brightness levels based on the habit preference analysis results. The decision tree model is constructed using Python's scikit-learn library. The input variables are the user's usage period and brightness preference, and the target variable is the matching habit type. The depth of the decision tree is set to 4 to prevent overfitting while maintaining the interpretability of the model. Information gain is used as the splitting criterion to analyze the relationship between the user's preferred brightness level and the usage period, and finally the user habit analysis results are obtained.

[0033] Specifically, if Figure 2 , 6As shown, the energy-saving dimming strategy module includes: The brightness preference analysis submodule uses the user habit analysis results to sort out the brightness preferences within the time period, identifies the low-frequency activity time period and the corresponding brightness preference through the correlation analysis between the user activity frequency and the brightness adjustment record, sets the threshold and target of the brightness adjustment, and obtains the execution process of the brightness preference setting as follows; The brightness preference analysis submodule adopts a cluster analysis algorithm based on the results of user habit analysis. The K-means algorithm is executed using the SciPy library to define clustering parameters, including setting the number of cluster centers to 3 and the maximum number of iterations to 300. The initialization method selects k-means++ to reduce the impact of initialization randomness. The K-means algorithm is used to process user activity frequency and brightness adjustment record data, identify brightness preferences in different time periods, set the brightness adjustment threshold to ±10% of the user's average brightness preference, and generate a target brightness adjustment range based on the time period when the user's activity frequency is 20% lower than the average activity frequency to generate a brightness preference setting.

[0034] The brightness adjustment execution submodule adopts a dynamic adjustment strategy based on the brightness preference setting. It automatically adjusts the brightness of the lamp by monitoring the matching degree between the current time and the set brightness preference. The adjustment range and speed are set according to the user preference and real-time situation. The execution process of generating dimming execution details is as follows; The brightness adjustment execution submodule adopts fuzzy logic control algorithm based on brightness preference setting, uses Matlab Fuzzy Logic Toolbox to perform fuzzy controller design, and defines fuzzy controller parameters, including input variables as the degree of matching between the current time and the brightness preference set by the user, and output variables as the brightness adjustment range and speed. The input variable membership function is set to a triangular membership function, and the output variable membership function is set to a trapezoidal membership function. The rule base is built based on if-then rules. For example, if the current time is close to the user's low-frequency activity period and the brightness is higher than the preference, the brightness is reduced. The brightness range and adjustment speed are flexibly adjusted according to the real-time environment and user preferences, and dimming execution details are generated.

[0035] The adjustment log recording submodule records the data of each dimming based on the dimming execution details, including the dimming time, the values ​​before and after the brightness adjustment, and the dimming reason, tracks the brightness adjustment effect, and generates the execution process of energy-saving dimming rules as follows; The adjustment log recording submodule is based on the dimming execution details and uses a relational database management system (specifically MySQL) to execute SQL commands for data recording and define the dimming log table structure, including fields set as dimming time DATETIME, value before brightness adjustment FLOAT, value after brightness adjustment FLOAT, and dimming reason VARCHAR(255). The INSERT INTO statement is used to record the detailed information of each dimming operation, including dimming time, values ​​before and after brightness adjustment, and dimming reason. The query statement SELECT FROM the dimming log table is used to track the brightness adjustment effect and generate energy-saving dimming rules.

[0036] Specifically, if Figure 2 , 7 As shown, the execution effect monitoring module includes: The energy consumption data analysis submodule records the energy consumption data after the implementation of the energy-saving dimming rules. If the data shows that the energy consumption is reduced, it is marked as the best energy-saving effect. By tracking the energy consumption change trend and comparing the energy consumption data difference before and after energy saving, the execution process of obtaining the energy consumption change analysis result is as follows; The energy consumption data analysis submodule is based on the energy consumption data after the implementation of the energy-saving dimming rules. It uses the time series analysis method to record the data, uses Python's Pandas library to time index the energy consumption data, and uses the sliding window averaging method to reduce data volatility. The window size is set to 7 days, and the average energy consumption is calculated on a daily basis. The average energy consumption before and after the implementation of the rules is compared. If the energy consumption is reduced, the set reduction percentage threshold is set to 10% to determine whether the optimal energy-saving effect standard is achieved. By tracking the trend of energy consumption changes, the Matplotlib library is used to draw an energy consumption change trend chart, and the difference in energy consumption data before and after energy saving is compared to obtain the energy consumption change analysis results.

[0037] The light color stability analysis submodule uses variance analysis method based on the energy consumption change analysis results to record the light color changes. If the light color remains within the user's preferred range, the light color stability is determined to be good. By tracking the light color stability in real time, the influence of the dimming rules on the light color stability is evaluated. The execution process of obtaining the light color stability evaluation results is as follows; The light color stability analysis submodule is based on the energy consumption change analysis results. The variance analysis method is applied to record the light color changes. The variance of the light color changes within the user preference range is calculated using the NumPy library. The user preference range is set to a color temperature of 2500K to 6500K. If the variance of the light color within this range is lower than the set threshold, the threshold is set to a variance value of 0.05, and the light color stability is judged to be good. By real-time tracking of the light color stability, real-time data stream processing technology is used, and Python's streamlit library is used to implement a real-time monitoring interface for light color stability. The impact of dimming rules on light color stability is evaluated, and the light color stability evaluation results are obtained.

[0038] The variance analysis method is based on the formula: ; in, is the adjusted variance, is the number of color temperature data points collected within the user's preferred range, is the color temperature value of each data point, is the average color temperature value of the data points, is the weight coefficient for each color temperature data point, is the current ambient temperature, is the optimal ambient temperature, is the current humidity, For optimal humidity, is the current light intensity, For the optimal light intensity, is the weight coefficient of ambient temperature, humidity and light intensity.

[0039] The execution process is as follows; Collect color temperature data and calculate its average value, assign weight coefficients to each color temperature data point to reflect the importance of data points in different time periods or under different environmental conditions, consider the impact of environmental factors on light color stability, multiply the square of the difference between the current ambient temperature, humidity and light intensity and their optimal values ​​by their respective weight coefficients, and add them to the variance calculation. By comparing the adjusted variance with the set threshold, the light color stability is judged. The weight coefficient can be confirmed through historical data analysis, such as by analyzing historical data on the impact of changes in different environmental factors on light color stability, and using statistical methods such as regression analysis to determine the weight coefficient of each environmental factor.

[0040] The monitoring data summary submodule summarizes the monitoring data of the lamps based on the light color stability evaluation results, integrates the energy consumption and light color stability data, and if the energy consumption and light color stability data meet the expected results, it is marked as having excellent execution effect. The execution process for generating the execution effect monitoring results is as follows; The monitoring data summary submodule summarizes the monitoring data of lamps based on the light color stability evaluation results using data integration technology, and uses the SQL database to perform JOIN operations to integrate energy consumption and light color stability data. The data integration rules are set as energy consumption reduction percentage greater than or equal to 10%, and light color stability variance less than or equal to 0.05. If both energy consumption and light color stability data meet this rule, the WHERE clause of the SQL query statement is used to filter the qualified data and mark it as having excellent execution effect. The filtering results are exported as CSV files using Python's pandas library to generate execution effect monitoring results.

[0041] Specifically, if Figure 2 , 8 As shown, the strategy adjustment feedback module includes: The effect analysis submodule uses the execution effect monitoring results to analyze energy consumption data and light color stability. By comparing the energy consumption difference and light color change before and after dimming, it identifies the impact of energy-saving measures and the effectiveness of light color control. The execution process of effect comparison and adjustment needs is as follows; The effect analysis submodule is based on the execution effect monitoring results, uses the time series analysis method, and uses the Pandas library and the Statsmodels library to analyze the energy consumption data and light color stability, and defines the time series model parameters, including setting the sliding window size to 7 days. The autoregressive moving average (ARIMA) model is used with the parameters set to (2, 1, 2), indicating that the autoregressive term is 2, the difference order is 1, and the moving average term is 2. The time series analysis method is used to compare the energy consumption difference and light color change before and after dimming, identify the impact of energy-saving measures and the effectiveness of light color control, and generate effect comparisons and adjustment needs.

[0042] The parameter adjustment record submodule analyzes the parameter changes required for the adjustment strategy based on the comparison of results and adjustment requirements, records the reasons, goals and expected impacts of each parameter adjustment, refines the selection and adjustment range of the adjustment parameters, and generates the execution process of the parameter adjustment log as follows; The parameter adjustment record submodule adopts a decision support system (DSS) based on effectiveness comparison and adjustment needs, writes a decision support algorithm in Python, and defines DSS parameters, including setting decision variables as adjustment ranges and adjustment targets. The decision rules are based on the effect analysis results. If the energy consumption reduction does not meet expectations, the adjustment range will be increased. The decision support system analyzes the parameter changes required for the adjustment strategy, records the reasons, goals and expected impacts of each parameter adjustment, refines the selection of adjustment parameters and the adjustment range, and generates a parameter adjustment log.

[0043] The optimization strategy recording submodule summarizes and analyzes the adjustment behavior and the expected optimization of energy saving effect and light color control according to the parameter adjustment log, and comprehensively evaluates the potential optimization direction of the adjusted strategy, including summarizing the effect of the adjustment measures, pointing out the content of the optimization measures, and generating the execution process of the overview of the comprehensive optimization plan as follows; The optimization strategy recording submodule uses the optimization algorithm, specifically the genetic algorithm, according to the parameter adjustment log. The DEAP library is used to perform genetic algorithm optimization and define the genetic algorithm parameters, including the population size is set to 50, the crossover probability is 0.7, the mutation probability is 0.2, and the number of iterations is set to 100. The genetic algorithm is used to summarize and analyze the adjustment behavior and energy-saving effect, and the expected optimization of light color control. The potential optimization direction of the adjusted strategy is comprehensively evaluated, including summarizing the effect of the adjustment measures, pointing out the content of the optimization measures, and generating an overview of the comprehensive optimization plan.

[0044] Although the present application has been described in conjunction with specific features and embodiments thereof, it is obvious that various modifications and combinations may be made thereto without departing from the spirit and scope of the present application. Accordingly, this specification and the drawings are merely exemplary illustrations of the present application as defined by the appended claims, and are deemed to have covered any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include these modifications and variations.

Claims

1. The LED lamp energy efficiency improvement system is characterized by: The system comprises: The light color stability analysis module collects the temperature and current intensity data of LED lamps during operation, conducts a direct correlation analysis of the effects of temperature and current intensity on light color, screens the light color change trend evaluation data, analyzes the relationship between temperature, current intensity and light color drift, determines the key influencing factors of light color change, conducts a direct correlation analysis of the effects of temperature and current intensity on light color, and obtains the light color change trend evaluation results; The current regulation strategy module adjusts the current intensity based on the light color change trend evaluation result. For the warm light color state, the current adjustment gradient is calculated to reduce the current intensity. For the cold light color state, the current intensity is increased and adjusted. When the light color is warm, the current is gradually reduced, and when the light color is cold, the current is increased to obtain a current adjustment parameter table. The usage mode analysis module collects the switch status and brightness adjustment records of the intelligent driving circuit based on the adjustment current parameter table, analyzes historical data, screens the frequency of use, switch cycle, and brightness adjustment behavior of the lamp, analyzes the distribution law of the usage mode, extracts the user operation tendency, collects the switch status and brightness adjustment records of the lamp, analyzes historical data to identify user habits, and obtains the user habit analysis results; The energy-saving dimming strategy module adjusts the brightness level of the LED lamp dimming module based on the user habit analysis results, analyzes the brightness adjustment range for the time period when the user's operation frequency is reduced, calculates the energy-saving dimming parameters, matches the user's operation preference, determines the energy-saving dimming parameter table, records the brightness adjustment details, adjusts the lamp brightness, adjusts the brightness for the user's frequency reduction period, automatically sets the brightness to match the user's preference, records the brightness adjustment details, and builds energy-saving dimming rules; The execution effect monitoring module monitors the energy consumption and light color stability data after the implementation of the energy-saving dimming rules, analyzes the impact of different dimming strategies on energy consumption changes, records light color changes, collects monitoring data, calculates energy consumption change trends, and screens light color stability fluctuation ranges. Through real-time data collection and recording, the monitoring data is summarized to obtain the execution effect monitoring results; The execution effect monitoring module includes: The energy consumption data analysis submodule records the energy consumption data after the implementation of the energy-saving dimming rule. If the data shows that the energy consumption is reduced, it is marked as the best energy-saving effect. By tracking the energy consumption change trend and comparing the energy consumption data difference before and after energy saving, the energy consumption change analysis result is obtained; The light color stability analysis submodule applies variance analysis method based on the energy consumption change analysis result to record the light color change. If the light color remains within the user preference range, the light color stability is determined to be good. By real-time tracking of the light color stability, the influence of the dimming rule on the light color stability is evaluated to obtain the light color stability evaluation result. The monitoring data summary submodule summarizes the monitoring data of the lamps based on the light color stability evaluation result, integrates the energy consumption and light color stability data, and if the energy consumption and light color stability data are in line with the expected effect, it is marked as having excellent execution effect, and generates the execution effect monitoring result; The ANOVA method is according to the formula: ； in, is the adjusted variance, is the number of color temperature data points collected within the user's preferred range, is the color temperature value of each data point, is the average color temperature of the data points, is the weight coefficient for each color temperature data point, is the current ambient temperature, is the optimal ambient temperature, is the current humidity, For optimal humidity, is the current light intensity, For the optimal light intensity, is the weight coefficient of ambient temperature, humidity and light intensity; Based on the execution effect monitoring results, the strategy adjustment feedback module analyzes the current strategy data, compares the changes in energy consumption and light color stability before and after the adjustment, filters abnormal data, determines the adaptability of energy-saving dimming rules, calculates the optimization adjustment coefficient, adjusts the dimming strategy of the intelligent drive control module, records the optimization parameters to analyze the effectiveness of the current strategy, adjusts the strategy through data comparison and analysis, records the optimization details, and obtains the strategy optimization plan.

2. The LED lamp energy efficiency improvement system according to claim 1, characterized in that: The light color change trend assessment results include parameters related to temperature rise and warm light color, parameters related to current intensity change and cool light color, and light color change trend indicators; the current adjustment parameter table includes current adjustment target value, current adjustment amplitude, and current adjustment strategy execution time point; the user habit analysis results include usage time period, preferred brightness level, and user habit matching score; the energy-saving dimming rules include automatic lamp brightness adjustment range, preset brightness adjustment schedule, and energy-saving target setting value; the execution effect monitoring results include energy saving rate, light color stability maintenance index, and user satisfaction score; the strategy optimization plan includes adjusted current control strategy parameters, lamp brightness adjustment optimization strategy, and expected energy efficiency optimization percentage.

3. The LED lamp energy efficiency improvement system according to claim 1, characterized in that: The light color stability analysis module comprises: The temperature and current monitoring submodule collects the temperature and current intensity data of the LED lamp during operation. If the temperature and current intensity exceed the preset threshold, they are adjusted to the standard range to generate temperature and current adjustment data. The light color deviation analysis submodule uses a linear regression analysis method based on the temperature and current adjustment data to analyze the relationship between temperature increase and warm light color, and between current intensity change and cold light color. If there is a correlation, the correlation model between temperature and warm light color, and between current intensity and cold light color is established to obtain the light color deviation correlation analysis result. The trend assessment submodule is based on the light color deviation correlation analysis result, and by comparing the light color changes under differentiated conditions, if the analysis shows a consistent trend toward warming or cooling, the trend is quantitatively assessed to obtain a light color change trend assessment result.

4. The LED lamp energy efficiency improvement system according to claim 3, characterized in that: The linear regression analysis method is according to the formula: ； in, is the light color deviation value, is the data after temperature adjustment, is the data after current adjustment, is the ambient brightness, For the duration of use, is the intercept term, is the slope parameter, is the interaction term between ambient brightness and usage duration, is the error term.

5. The LED lamp energy efficiency improvement system according to claim 1, characterized in that: The energy-saving dimming strategy module includes: The brightness preference analysis submodule uses the user habit analysis results to sort out the brightness preferences within the time period, identifies the low-frequency activity time period and the corresponding brightness preference through the correlation analysis between the user activity frequency and the brightness adjustment record, sets the threshold and target of the brightness adjustment, and obtains the brightness preference setting; The brightness adjustment execution submodule adopts a dynamic adjustment strategy based on the brightness preference setting, automatically adjusts the brightness of the lamp by monitoring the matching degree between the current time and the set brightness preference, and sets the adjustment range and speed according to the user preference and real-time situation, and generates dimming execution details; The adjustment log recording submodule records the data of each dimming operation based on the dimming execution details, including the dimming time, the values ​​before and after the brightness adjustment, and the dimming reason, tracks the brightness adjustment effect, and generates energy-saving dimming rules.

6. The LED lamp energy efficiency improvement system according to claim 1, characterized in that: The strategy adjustment feedback module includes: The effect analysis submodule uses the execution effect monitoring results to analyze energy consumption data and light color stability, and by comparing the energy consumption difference and light color change before and after dimming, identifies the impact of energy-saving measures and the effectiveness of light color control, and obtains effect comparison and adjustment needs; The parameter adjustment record submodule analyzes the parameter changes required for the adjustment strategy based on the effectiveness comparison and adjustment requirements, records the reasons, goals and expected impacts of each parameter adjustment, refines the selection and adjustment range of the adjustment parameters, and generates a parameter adjustment log; The optimization strategy recording submodule adjusts the parameter log, summarizes and analyzes the adjustment behavior and the expected optimization of energy saving effect and light color control, and comprehensively evaluates the potential optimization direction of the adjusted strategy, including summarizing the effect of the adjustment measures, pointing out the content of the optimization measures, and generating an overview of the comprehensive optimization plan.

Images