Intelligent energy-saving control method and system for LED lamp

Abstract

The invention provides an LED lamp intelligent energy-saving control method and system, and the method comprises the steps: obtaining environment illumination intensity data, personnel flow density data and current working state parameters of an LED lamp, which are collected by a plurality of sensors, carrying out the preprocessing of the original data of the sensors, and obtaining a standardized environment perception data set; calculating power resources required by the intelligent control system are evaluated according to the LED lamp control parameter optimization scheme, the brightness output of the LED lamp is controlled according to the calculating power resources, and the current proportion of red, green and blue LED chips is adjusted according to the color temperature; whether the light beam angle needs to be adjusted or not is judged through matching analysis of the light beam angle and environmental perception data, if yes, the lens position of the LED lamp is changed, and the output spectral characteristics of the LED lamp are measured and determined through a spectrum analyzer; after sensor calibration parameters are updated, LED lamp power consumption data are collected, and if the deviation between actual power consumption and a theoretical calculation value is smaller than a preset percentage, it is determined that the energy-saving control effect reaches an expected target.

Description

Technical Field

[0001] The present invention relates to the field of information technology, and in particular to an intelligent energy-saving control method and system for LED lamps. Background Art

[0002] As a crucial component of modern urban infrastructure, intelligent lighting systems play a vital role in energy conservation, emission reduction, and improving user experience. With accelerating urbanization and rising energy costs, efficient lighting control technology has become a core element of smart city development. Current mainstream lighting control solutions primarily rely on cloud computing and centralized decision-making architectures, performing well in environments with stable network connections. However, these solutions often fail in offline scenarios, unable to achieve localized, real-time responses. They also have limited adaptability to complex, dynamic environments and struggle to handle multi-source interference and sensor data fusion. In practical applications, dynamic scene transitions trigger cascading changes in the lighting environment, exposing fundamental flaws in traditional control methods. When densely populated areas, such as subway platforms, experience sudden surges in light demand, external light sources, particularly traffic lights, can continuously interfere with environmental sensors, leading to skewed data. This interference accumulates in complex environments, gradually exacerbating sensor calibration errors. Calibration errors directly impact the system's ability to accurately assess environmental conditions, leading to an imbalance in the allocation of computing resources across different control tasks. How to build a real-time and robust localized decision-making mechanism in offline scenarios, overcome external interference and calibration deviation problems by effectively fusing multi-source sensor data, and achieve precise dynamic adjustment of LED brightness, color temperature, and beam angle under the constraints of limited computing resources, and fully utilize the instantaneous response characteristics and wide spectrum tuning capabilities of LEDs, has become a key issue that urgently needs to be broken through in the field of intelligent energy-saving lighting control. Summary of the Invention

[0003] The present invention provides an intelligent energy-saving control method for LED lamps, which mainly includes:

[0004] Acquire ambient light intensity data, personnel flow density data, and current working status parameters of LED lights collected by multiple sensors, pre-process the raw sensor data, and obtain a standardized environmental perception data set;

[0005] The crowd density change trend is predicted based on the historical personnel flow density data. If the crowd density shows an upward trend within a continuous preset time window, the population density change trend is input into the dynamic lighting demand prediction model established based on the standardized environmental perception data set, and the output is the lighting demand increment coefficient;

[0006] The target brightness, color temperature and beam angle parameters of each LED light node are determined by the incremental coefficient of light demand. If the difference between the target brightness value and the current brightness value exceeds a preset percentage, the color temperature and beam angle parameters are adjusted to generate an optimized solution for the LED light control parameters.

[0007] Evaluate the computing power resources required for the intelligent control system based on the LED light control parameter optimization plan, control the LED light brightness output based on the computing power resources, and adjust the current ratio of the red, green, and blue LED chips according to the color temperature;

[0008] The beam angle is matched with the environmental perception data to determine whether the beam angle needs to be adjusted. If so, the lens position of the LED lamp is changed, and the output spectrum characteristics of the LED lamp are measured using a spectrum analyzer.

[0009] The sensor measurement deviation is determined by comparing the output spectral characteristic data of the LED lamp with the preset standard spectral data, and the sensor measurement deviation is compensated and calculated. If the accumulated value of the calibration deviation exceeds the preset error range, the sensor calibration parameters are updated;

[0010] After updating the sensor calibration parameters, the power consumption data of the LED lamp is collected. If the deviation between the actual power consumption and the theoretical calculated value is less than the preset percentage, it is determined that the energy-saving control effect has achieved the expected goal.

[0011] The present invention provides an intelligent energy-saving control system for LED lamps, which mainly includes:

[0012] The data preprocessing module is used to obtain the ambient light intensity data, personnel flow density data and current working status parameters of LED lights collected by multiple sensors, preprocess the raw sensor data, and obtain a standardized environmental perception data set;

[0013] The trend prediction module is used to predict the trend of crowd density changes based on historical crowd flow density data. If the crowd density shows an upward trend within a continuous preset time window, the crowd density change trend is input into the dynamic lighting demand prediction model established based on the standardized environmental perception data set, and the output is the lighting demand increment coefficient;

[0014] The lighting demand prediction module is used to determine the target brightness, color temperature, and beam angle parameters of each LED lamp node through the lighting demand increment coefficient. If the difference between the target brightness value and the current brightness value exceeds a preset percentage, the color temperature and beam angle parameters are adjusted to generate an LED lamp control parameter optimization plan;

[0015] The parameter optimization module is used to evaluate the computing power resources required for the intelligent control system based on the LED light control parameter optimization plan, control the LED light brightness output based on the computing power resources, and adjust the current ratio of the red, green and blue LED chips according to the color temperature;

[0016] The computing power control module is used to determine whether the beam angle needs to be adjusted by matching the beam angle with the environmental perception data. If so, the lens position of the LED lamp is changed and the output spectrum characteristics of the LED lamp are measured by the spectrum analyzer.

[0017] The beam adjustment module is used to determine the sensor measurement deviation by comparing the spectral characteristic data of the LED light output with the preset standard spectral data, and to compensate for the sensor measurement deviation. If the accumulated value of the calibration deviation exceeds the preset error range, the sensor calibration parameters are updated;

[0018] The calibration evaluation module is used to collect the power consumption data of the LED lamp after updating the sensor calibration parameters. If the deviation between the actual power consumption and the theoretical calculated value is less than a preset percentage, it is determined that the energy-saving control effect has achieved the expected goal.

[0019] The technical solution provided by the embodiment of the present invention may have the following beneficial effects:

[0020] The present invention discloses an intelligent energy-saving control method and system for LED lamps. The method collects and pre-processes environmental data through multiple sensors, establishes a dynamic lighting demand model based on the crowd density prediction trend, and outputs a lighting demand increment coefficient. The target brightness, color temperature, and beam angle parameters of the LED lamp are determined based on the coefficient, and a control parameter optimization scheme is generated. The present invention controls the output of the LED lamp in real time through pulse width modulation and current proportional regulation technology, and uses a mechanical adjustment device to change the lens position to adjust the beam angle. At the same time, the sensor measurement deviation is compensated and calibrated through spectral analysis, and the energy consumption data is monitored in real time to evaluate the energy-saving effect. The method realizes intelligent lighting control based on environmental perception, can dynamically adjust lighting parameters, improve lighting efficiency and energy-saving effects, and is suitable for lighting system optimization in scenarios such as smart cities. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 This is a flow chart of an intelligent energy-saving control method for LED lamps according to the present invention.

[0022] Figure 2 This is a structural diagram of an intelligent energy-saving control system for LED lamps according to the present invention. DETAILED DESCRIPTION

[0023] In order to make the objectives, technical solutions and advantages of the present invention more clear, the present invention is described in detail below with reference to the accompanying drawings and specific embodiments.

[0024] like Figure 1-2 In this embodiment, an intelligent energy-saving control method for LED lamps may specifically include:

[0025] S101. Acquire ambient light intensity data, personnel flow density data, and current working status parameters of LED lights collected by multiple sensors, pre-process the sensor raw data, and obtain a standardized environmental perception data set.

[0026] The raw values of ambient light intensity, personnel flow density, and LED light operating state parameters are obtained through a multi-channel sensor interface. A data cleaning method is used to detect outliers and filter noise on the raw values to obtain a first light intensity value, a first flow density value, and a first operating state parameter. The first light intensity value is normalized using a minimum-maximum normalization method based on its range. The first flow density value is normalized using a Z-score normalization method based on its distribution characteristics. The first operating state parameter is normalized using a one-hot encoding method based on its categorical attributes to obtain a second light intensity value, a second flow density value, and a second operating state parameter. If the difference between the timestamp of the second light intensity value and the timestamp of the second flow density value is less than a preset time threshold, the second light intensity value and the second flow density value are aligned in the time dimension using a time synchronization alignment method. If the difference between the sampling frequency of the second operating state parameter and the sampling frequency of the matched data is less than a preset frequency threshold, the second operating state parameter and the matched data are aligned in the frequency dimension using a frequency synchronization method to obtain synchronized multidimensional data. A data fusion method is used to weight and merge the illumination dimension, traffic dimension and status dimension in the synchronized multidimensional data according to preset weighting coefficients. Through data structure reorganization, the merged multidimensional data is arranged and organized according to time series order and spatial position information to obtain a standardized environmental perception data set.

[0027] For example, in an intelligent lighting control scenario, a multi-channel sensor interface simultaneously connects to a photoresistor sensor, an infrared human detection sensor, and an LED driver status monitoring module. The photoresistor sensor collects raw values of ambient light intensity once per second, typically ranging from 0 to 1023. The infrared human detection sensor measures the raw value of occupant density in the area every two seconds. The LED driver status monitoring module outputs raw operating parameters such as current operating current, voltage, and brightness in real time. This multi-channel parallel acquisition method enables the simultaneous acquisition of all the basic data sources required for environmental perception.

[0028] In one possible implementation, the data cleaning method uses the 3σ criterion to identify abnormal data points outside the normal range when performing outlier detection on the raw values. If the raw light intensity value suddenly jumps from 200 to above 900, the system identifies it as an outlier and replaces it with the average of the preceding and following values. Noise filtering uses a sliding average window method to average the values over five consecutive moments, eliminating data fluctuations caused by sensor jitter or environmental interference. The resulting first light intensity value, first flow density value, and first operating status parameter have higher data quality and reliability after the cleaning process.

[0029] It should be noted that different types of sensor data need to be processed using corresponding standardization methods. Since the first light intensity value has a clear physical dimension and numerical boundary, the minimum-maximum normalization method maps it to the standard range of 0 to 1. The calculation formula is that the standardized value is equal to the original value minus the minimum value and divided by the difference between the maximum and minimum values. Since the first flow density value exhibits normal distribution characteristics, the Z-score standardization method converts the data into a standard normal distribution with a mean of 0 and a standard deviation of 1 by subtracting the mean and dividing by the standard deviation. The first working state parameter belongs to discrete categorical data. The one-hot encoding method converts each state category into a binary vector representation. For example, the three states of the LED light on, off, and dimming are encoded as [1,0,0], [0,1,0], and [0,0,1] respectively.

[0030] In one embodiment, this differentiated normalization strategy ensures that sensor values of different physical dimensions and data types can be subsequently processed in a unified numerical space, avoiding data fusion bias problems caused by dimensional differences.

[0031] Specifically, the second light intensity value and the second flow density value have inconsistent timestamps because they come from sensors with different sampling frequencies. The time synchronization alignment method uses an interpolation algorithm to align data points at different sampling times to a unified time base. When the light sensor collects data at 10:00:00 and the flow sensor collects data at 10:00:01, the system calculates the difference in timestamps between the two to be 1 second. If this difference is less than the preset time threshold of 3 seconds, the two data points are considered to belong to the same environmental state at the same time, and the time dimension is matched.

[0032] Preferably, the frequency synchronization method addresses the difference in sampling frequency between the second operating state parameter and the matched data. The LED driver status monitoring module samples 10 times per second, while the time-matched environmental data is sampled once per second. The system uses downsampling technology to adjust the high-frequency data to the sampling rhythm of the low-frequency data, ensuring that all data dimensions are fully aligned on the time axis.

[0033] For example, the data fusion method combines the illumination, flow, and status dimensions in the synchronized multidimensional data according to preset weighting coefficients. In the office environment application scenario, the weighting coefficient of the illumination dimension is set to 0.4, the weighting coefficient of the flow dimension is set to 0.4, and the weighting coefficient of the status dimension is set to 0.2. This weight distribution reflects the dominant role of illumination and personnel flow in environmental perception. The weighted merging process generates a comprehensive environmental perception index through linear combination, which can comprehensively reflect the overall status characteristics of the current environment. The data structure reorganization further arranges the merged multidimensional data into a matrix according to the chronological order of the time series and the spatial location information of the sensors, forming a standardized environmental perception data set with spatiotemporal attributes, providing a structured data foundation for subsequent intelligent decision-making.

[0034] S102. Predict the changing trend of crowd density based on historical personnel flow density data. If the crowd density shows an upward trend within a continuous preset time window, the changing trend of crowd density is input into the dynamic lighting demand prediction model established based on the standardized environmental perception data set, and the output is the lighting demand increment coefficient.

[0035] Based on the historical personnel flow density data in the standardized environmental perception data set, a time series analysis method is used to construct a personnel flow density prediction sequence. The flow density values within consecutive time periods are trend analyzed and processed using a sliding window mechanism. If the flow density values within a preset number of consecutive time windows show an increasing pattern, the personnel flow density change trend is determined to be an upward state, and a flow density upward trend parameter is obtained. Using the flow density upward trend parameter as a trigger condition, the personnel flow density change trend within the corresponding time period is correlated and matched with the historical light intensity data in the standardized environmental perception data set. A mapping relationship between personnel flow density changes and light demand is established using a linear regression method to obtain a dynamic light demand prediction model. The dynamic light demand prediction model takes the personnel flow density change trend as the input parameter and outputs the light demand prediction value. According to the dynamic light demand prediction model, the light demand is calculated using the current personnel flow density change trend as the input parameter. If the personnel flow density change trend shows a continuous upward trend, the linear regression coefficient in the dynamic light demand prediction model is used to perform an incremental adjustment calculation on the current environmental basic light demand to obtain a light demand adjustment value. The light demand adjustment value is converted into a standardized coefficient form through a normalization processing method, and the ratio of the light demand adjustment value to the current environment basic light demand is calculated using a numerical mapping method to obtain a light demand increment coefficient.

[0036] For example, in an office building smart lighting scenario, a time series analysis method constructs a prediction sequence based on historical data on occupant density collected every 15 minutes over the past 24 hours. This method identifies the direction of data change by calculating the difference between values at adjacent time points. A clear increasing pattern is identified when the occupant density values within three consecutive time windows increase from 0.8 people per square meter at 8:00 a.m. to 1.2 people per square meter at 8:45 a.m. and then to 1.6 people per square meter at 9:30 a.m. The sliding window mechanism plays a key role in this process. It continuously moves along the timeline in fixed-length time windows, each covering a 45-minute data collection period. The trend direction is determined by the slope of the linear fit of the data points within the window.

[0037] In one possible implementation, trend analysis uses a moving average cross-validation method to enhance judgment accuracy. When the short-term moving average consistently lies above the long-term moving average, and the gap between them gradually widens, the system confirms that the traffic density trend is continuously increasing. This dual verification mechanism effectively avoids misjudgments due to occasional fluctuations and ensures the reliability of the traffic density upward trend parameter.

[0038] It's important to note that the rising traffic density trend parameter serves as a trigger to initiate the construction of the lighting demand forecasting model. This process first establishes a temporal relationship between changes in traffic density and historical light intensity data. Using a data matching algorithm, the traffic density changes within the same time period are paired with the corresponding light intensity adjustment records.

[0039] For example, when historical data shows that the personnel flow density increases from 1.2 people per square meter to 1.8 people per square meter between 9 and 10 a.m., the corresponding light intensity is adjusted from 300 lux to 450 lux, and the system records this corresponding relationship as a training sample.

[0040] In one embodiment, a linear regression method uses the least squares method to determine the mathematical mapping between changes in occupancy density and lighting requirements. This method calculates the slope coefficient of the regression line, which represents the corresponding increase in lighting requirements for each unit increase in occupancy density. The dynamic lighting demand prediction model uses the real-time trend of occupancy density as input and the predicted adjustment to lighting requirements as output. The model accurately predicts future lighting requirements by learning regular associations from historical data.

[0041] Specifically, the dynamic lighting demand prediction model receives the current trend of occupant density as input and calculates the corresponding lighting demand forecast using an established linear regression relationship. If the input density trend shows an increase of 0.6 people per square meter, the model calculates a corresponding increase of 120 lux in lighting demand based on the regression coefficient. The linear regression coefficient plays a key role in this process, translating the abstract change in occupant density into a specific adjustment in lighting intensity.

[0042] For example, when the current ambient base lighting requirement is set at 400 lux, the system adds the 120 lux lighting demand adjustment value output by the model to the base requirement, resulting in a total lighting demand of 520 lux. This adjustment value is then converted into a ratio relative to the base requirement through a normalization process, using a normalization coefficient of 0.3 calculated by dividing 120 by 400. A numerical mapping method further converts this ratio into a lighting demand increment coefficient, which directly reflects the additional lighting demand intensity generated by the increased traffic density, providing a quantitative adjustment basis for subsequent intelligent lighting control.

[0043] S103. Determine the target brightness, color temperature, and beam angle parameters of each LED lamp node through the incremental coefficient of light demand. If the difference between the target brightness value and the current brightness value exceeds a preset percentage, adjust the color temperature and beam angle parameters to generate an LED lamp control parameter optimization plan.

[0044] According to the incremental coefficient of the lighting demand, a proportional calculation method is used to determine the target brightness value of each LED lamp node, and the incremental coefficient is matched with the corresponding brightness value through a preset brightness mapping relationship table. At the same time, an interpolation calculation method is used to determine the corresponding target color temperature value and target beam angle value according to the target brightness value, and a target parameter set of the LED lamp node is obtained. The target brightness value in the target parameter set of the LED lamp node is used to perform a difference calculation with the current brightness value of the LED lamp. If the percentage difference between the target brightness value and the current brightness value exceeds the preset adjustment threshold, a linear interpolation method is used to perform a gradual adjustment process on the target color temperature value, and at the same time, the target beam angle value is adjusted accordingly according to the brightness difference ratio to obtain an adjusted target parameter set. The brightness adjustment value is obtained by calculating the difference between the target brightness value in the adjusted target parameter set and the current brightness value, and the color temperature conversion method is used to convert the adjusted target color temperature value into a cold and warm color temperature ratio value. The adjusted target beam angle value is converted into a beam angle adjustment amount through angle difference calculation to obtain an LED lamp control parameter set. According to the brightness adjustment value, warm and cold color temperature ratio value and beam angle adjustment amount in the LED lamp control parameter set, a data encapsulation method is used for structured organization. Through control instruction conversion, the parameter values are converted into an instruction format recognized by the LED lamp driver to obtain an LED lamp control parameter optimization solution.

[0045] For example, the proportional calculation method determines parameters based on the linear relationship between the incremental coefficient of light demand and the brightness output of the LED lamp. A light demand incremental coefficient of 0.3 indicates a 30% increase in light intensity over the current level. A preset brightness mapping table stores target brightness values corresponding to different incremental coefficients. This table, established through extensive experimental data, records the standard brightness output values corresponding to each incremental coefficient in the range of 0.1 to 1.0.

[0046] For example, an increment coefficient of 0.3 corresponds to a target brightness of 130% of the current brightness, and an increment coefficient of 0.5 corresponds to a target brightness of 150%.

[0047] In one possible implementation, an interpolation calculation method solves the problem of coordinating the configuration of color temperature, beam angle, and brightness parameters. Based on the human eye's visual perception, this method adjusts the color temperature toward cool white to enhance the lighting effect as the target brightness increases, while narrowing the beam angle to improve light concentration.

[0048] Specifically, when the target brightness increases from 800 lumens to 1040 lumens, the corresponding target color temperature is adjusted from 3000K to 4000K, and the target beam angle is narrowed from 60 degrees to 45 degrees. This coordinated configuration ensures the overall optimization of the lighting effect.

[0049] It should be noted that the difference percentage calculation is the key basis for determining whether color temperature and angle adjustments are necessary. When the difference between the target brightness value and the current brightness value exceeds the preset adjustment threshold, the system initiates the fine-tuning process. This threshold is typically set at 15%, which is based on the human eye's sensitivity to changes in lighting. Changes below 15% are difficult for the human eye to detect, while changes above 15% require corresponding adjustments to color temperature and angle.

[0050] In one embodiment, the linear interpolation method avoids visual discomfort caused by sudden changes in color temperature through gradual adjustment. When the brightness difference is 25%, the system does not jump directly to the target color temperature, but adjusts gradually in multiple stages.

[0051] For example, when adjusting from the current color temperature of 3500K to the target color temperature of 4200K, the interpolation method divides the entire adjustment process into five stages, with each stage adjusting by 140K, to achieve a smooth color temperature transition. The beam angle is also adjusted gradually, avoiding sudden changes in the lighting range that may cause user disturbance.

[0052] Specifically, the brightness adjustment value is calculated by directly calculating the difference between the target brightness and the current brightness. This value represents the specific amount by which the LED lamp drive current needs to be increased or decreased. A color temperature conversion method is used to convert the Kelvin temperature value of the color temperature into the ratio of the red, green, and blue color channels of the LED lamp. The warm and cool color temperature ratio value is expressed as a percentage to represent the drive strength of each color channel.

[0053] For example, a color temperature of 4000K corresponds to a driving ratio of 70% for the red channel, 85% for the green channel, and 100% for the blue channel.

[0054] For example, the beam angle adjustment is converted into physical adjustment parameters for the LED light's optical lens by calculating the angle difference. To adjust the beam angle from 60 degrees to 45 degrees, the angle adjustment is minus 15 degrees, which instructs the lens drive motor to make the corresponding convergence adjustment. This quantitative representation ensures that abstract optical parameters can be translated into concrete hardware control instructions.

[0055] In one possible implementation, a data encapsulation method organizes discrete control parameters into a structured data packet format. This method arranges the brightness adjustment value, the warm and cool color temperature ratio, and the beam angle adjustment in a specific order according to the LED lamp controller's data reception protocol, and adds necessary checksums and synchronization signals. Control instruction conversion then converts these parameter values into a binary instruction format that the LED lamp driver can directly recognize and execute.

[0056] It's important to note that the generated LED light control parameter optimization solution includes a complete set of lighting adjustment instructions, including not only specific parameter values but also execution timing and priority information. This comprehensive optimization solution ensures that multiple LED light nodes can coordinate and respond to changes in traffic flow, achieving intelligent adaptation to the lighting environment.

[0057] The dynamic range of lighting demand is calculated through the lighting demand increment coefficient. The energy-saving indicators of energy consumption ratio and brightness efficiency are obtained based on the dynamic range of lighting demand. The lighting demand is restrictively adjusted according to the energy-saving indicators. The adjusted lighting demand value is determined, and the target brightness parameter is calculated based on the demand value. The current color temperature configuration is predicted in combination with the preset range of color temperature parameters, and the beam angle parameter is adjusted according to the prediction result.

[0058] The dynamic range of the lighting demand is determined using a numerical range calculation method based on the lighting demand increment coefficient. The corresponding power consumption difference and brightness output difference are calculated based on the maximum and minimum demand values within the dynamic range. The energy consumption ratio index and brightness efficiency index are calculated using the ratio of the power consumption difference to the brightness output difference to obtain an energy-saving evaluation index set. The energy consumption ratio index and brightness efficiency index in the energy-saving evaluation index set are compared with preset energy-saving standards. If the energy consumption ratio index corresponding to the lighting demand exceeds the preset energy consumption limit value, the lighting demand is adjusted downward using a proportional reduction method. If the brightness efficiency index is lower than the preset efficiency reference value, the lighting demand is adjusted upward using a proportional increase method to obtain an energy-saving optimized lighting demand value. The energy-saving optimized lighting demand value is used to calculate the corresponding target brightness parameter through a photoelectric conversion relationship. A regression algorithm is used to establish a mathematical mapping relationship between the target brightness parameter and the color temperature parameter in combination with a preset color temperature parameter range. The optimal color temperature parameter corresponding to the current target brightness parameter is predicted and calculated using the mathematical mapping relationship to obtain a predicted color temperature parameter value. An optical calculation method is used to determine the most suitable beam angle parameter based on the combined relationship between the predicted color temperature parameter value and the target brightness parameter. The calculated beam angle parameter is converted into an angle adjustment control signal of the LED light optical component by a beam control unit to obtain a beam angle parameter adjustment control scheme.

[0059] Exemplarily, the numerical range calculation method establishes a dynamic demand boundary based on the incremental coefficient of the lighting demand. When the incremental coefficient is 0.4, this value means that an additional 240 lumens are required on the basis of the basic lighting demand of 600 lumens. The maximum and minimum demand values are determined by analyzing the volatility of the incremental coefficient. Taking into account that the incremental coefficient may vary between 0.2 and 0.6 in actual applications, the corresponding lighting demand range is from 720 lumens to 960 lumens. The power consumption difference is calculated through the light efficiency conversion relationship of the LED lamp. When the light output increases from 720 lumens to 960 lumens, the corresponding power consumption increases from 12 watts to 16 watts, and the power consumption difference is 4 watts.

[0060] In one possible implementation, the energy efficiency ratio is calculated as the ratio of the power consumption difference to the light output difference. This ratio reflects the energy cost per unit of light output increase. For example, when a 4-watt power consumption difference corresponds to a 240-lumen light output difference, the energy efficiency ratio is 0.0167 watts per lumen. The luminance efficiency ratio is calculated as the inverse ratio of the light output difference to the power consumption difference, resulting in a value of 60 lumens per watt. This metric directly reflects the energy efficiency of the LED lamp during dynamic adjustment.

[0061] It should be noted that the energy-saving evaluation index set provides a quantitative basis for subsequent demand optimization. Preset energy-saving standards are determined based on building energy-saving regulations and LED lamp technical parameters. The preset energy consumption limit is typically set at 0.02 watts per lumen, and the preset efficiency benchmark is set at 50 lumens per watt. By comparing the real-time calculated indicators with the preset standards, if the energy consumption ratio indicator of 0.0167 watts per lumen is lower than the limit of 0.02 watts per lumen, it indicates that the current lighting demand is within the energy consumption control range.

[0062] In one embodiment, the proportional reduction method and the proportional increase method provide a bidirectional demand adjustment mechanism. When it is detected that the energy consumption ratio index exceeds the limit value, the proportional reduction method proportionally reduces the lighting demand according to the excess ratio.

[0063] For example, if the calculated lighting requirement is 1,000 lumens but the corresponding energy consumption ratio exceeds the standard by 20%, the reduction method adjusts the requirement to 800 lumens. Conversely, when the brightness efficiency index falls below the baseline value, the proportional increase method appropriately increases the lighting requirement without exceeding the energy consumption limit, ensuring a balance between lighting performance and energy conservation requirements.

[0064] Specifically, the photoelectric conversion relationship calculation converts the required illumination into a brightness parameter based on the photometric properties of LED lamps. This conversion takes into account the luminous efficiency of the LED chip, the optical loss of the lens, and the spatial distribution characteristics of the illumination. When the illumination requirement after energy-saving optimization is 850 lumens, the photoelectric conversion relationship calculation results in a target brightness parameter of 85 candelas per square meter. Based on this, a regression algorithm is used to establish a mathematical mapping between the target brightness parameter and the color temperature parameter. This algorithm analyzes the pairing relationship between brightness and color temperature in a large amount of historical data to identify linear or nonlinear correlation patterns between the two.

[0065] For example, the mathematical mapping relationship represents the variation of the color temperature parameter with the target brightness parameter. When the target brightness parameter varies between 60 and 100 candelas per square meter, the optimal color temperature parameter is adjusted accordingly from 3500K to 4500K. This mapping relationship is based on the dual considerations of human visual comfort and lighting optimization, ensuring the most suitable light and color environment at different brightness levels.

[0066] In one possible implementation, an optical calculation method determines the beam angle by comprehensively considering the combined effects of the predicted color temperature parameter value and the target brightness parameter. Based on optical principles, this method analyzes the optimal light distribution pattern for different color temperature and brightness combinations. When the predicted color temperature parameter value is 4200K and the target brightness parameter is 85 candelas per square meter, the optical calculation determines the most suitable beam angle parameter to be 45 degrees. This angle ensures sufficient lighting coverage while avoiding excessive light dispersion.

[0067] It's important to note that the beam control unit, acting as the actuator, converts the calculated results into actual hardware adjustments. After receiving the beam angle parameters, this unit generates corresponding control signals to drive the optical components within the LED lamp for physical adjustment. The beam angle parameter adjustment control scheme includes precise angle values, adjustment timing, and execution priority, ensuring that multiple LED lamp nodes can coordinate and dynamically optimize beam angles.

[0068] S104. Evaluate the computing power resources required for the intelligent control system according to the LED light control parameter optimization plan, control the brightness output of the LED light according to the computing power resources, and adjust the current ratio of the red, green and blue LED chips according to the color temperature.

[0069] Based on the brightness adjustment value, color temperature ratio value, and beam angle adjustment value in the LED light control parameter optimization scheme, a computational complexity assessment method is used to calculate the processor computing load and memory usage required by the intelligent control unit. The processing capacity of the concurrent control requirements of multiple LED light nodes is evaluated through multi-task load analysis to obtain controller computing power resource demand data. The controller computing power resource demand data is used to determine the processing capacity matching. If the controller computing power resource demand data exceeds the preset processing capacity of the control unit, the LED light control tasks are time-sequentially allocated through task queue management. A pulse width modulation signal generator generates a control pulse signal with a corresponding duty cycle based on the brightness adjustment value to obtain an LED light brightness modulation signal sequence. The LED light brightness modulation signal sequence is used to drive the LED chip to control the brightness output. At the same time, the color temperature ratio value is used to calculate the driving current distribution ratio of the red LED chip, the green LED chip, and the blue LED chip through a three-way current controller. The color temperature ratio value is converted into the driving current setting value of each color LED chip through current ratio conversion to obtain the current distribution parameters of the RGB three-color chips. The red, green and blue LED chips are respectively driven with precise current by a constant current source circuit through the current distribution parameters of the RGB three-color chip. The angle position change of the LED lamp lens assembly is controlled by an optical adjustment device according to the beam angle adjustment amount to obtain the complete output control execution state of the LED lamp.

[0070] For example, the computational complexity assessment method determines system load based on the data volume and processing frequency of LED light control parameters. When a single LED light node needs to process three parameters: brightness adjustment value, color temperature ratio value, and beam angle adjustment value, each parameter requires 4 bytes of storage space and approximately 10 microseconds of processing time. Multi-task load analysis calculates overall resource requirements by counting the number of LED light nodes controlled simultaneously. When a smart lighting area contains 50 LED light nodes, the processor needs to process 150 control parameters simultaneously, corresponding to a memory usage of 600 bytes and a total processing time of 500 microseconds.

[0071] In one possible implementation, controller computing resource demand data provides a quantitative basis for subsequent task scheduling. This data not only includes computational workload statistics but also takes into account real-time requirements and concurrent processing capabilities. When the control unit uses an ARM Cortex-M4 processor, its preset processing capacity is 10,000 control instructions per second, but actual demand may reach 15,000 per second, exceeding the preset capacity by 50%. This resource demand assessment provides a clear trigger for initiating task queue management.

[0072] It's important to note that task queue management addresses insufficient processing capacity through time allocation. This management mechanism prioritizes control tasks that exceed processing capacity, prioritizing urgent brightness adjustment tasks and deferring less urgent angle adjustment tasks. After receiving the brightness adjustment value, the pulse-width modulation signal generator converts the value into a time-proportional relationship through duty cycle calculation.

[0073] For example, when the brightness adjustment value requires the LED lamp to output 80% of the maximum brightness, the duty cycle of the PWM signal is set to 80%, that is, in each 10 microsecond cycle, the high level lasts for 8 microseconds and the low level lasts for 2 microseconds.

[0074] In one embodiment, a signal sequence for LED light brightness modulation provides a precise timing reference. This sequence not only controls the on and off timing of the LED chips but also ensures synchronization and coordination between multiple LED light nodes. If 50 LED light nodes need to adjust their brightness simultaneously, the signal sequence, using a unified clock reference, ensures that all nodes begin the modulation process at the same time, avoiding flickering caused by timing discrepancies.

[0075] Specifically, the three-way current controller precisely adjusts color temperature based on the electro-optical conversion characteristics of the LED chips. At a 3.2V operating voltage, the maximum drive current for a red LED chip is 350 mA, for a green LED chip it is 300 mA, and for a blue LED chip it is 280 mA. When the color temperature ratio requires a warm white light of 4000K, the three-way current controller calculates that the red LED chip requires a drive current of 280 mA, the green LED chip requires a drive current of 300 mA, and the blue LED chip requires a drive current of 240 mA.

[0076] For example, the current proportional conversion process ensures a linear correspondence for color temperature adjustment. This conversion, based on the principles of colorimetry, converts abstract color temperature values into specific current settings. The RGB chip current distribution parameters not only include the driving current values for each color LED chip, but also include the slope control parameters for the current change, ensuring a smooth transition during color temperature adjustment. When the color temperature is adjusted from 3000K to 5000K, the current distribution parameters control the current of the three-color LED chips to gradually change according to the preset curve, avoiding color jumps.

[0077] In one possible implementation, a constant current source circuit ensures stable operation of the LED chip through a feedback control mechanism. This circuit monitors voltage changes across the LED chip. When the internal resistance of the LED chip decreases due to rising ambient temperature, the constant current source automatically adjusts the output voltage to maintain a constant set current. The optical adjustment device synchronizes with the amount of beam angle adjustment, using a stepper motor to drive the lens assembly for micron-level position adjustment.

[0078] It's important to note that the complete output control execution state of an LED lamp represents the coordinated and unified performance of brightness, color temperature, and beam angle. This state not only reflects the current output parameter settings but also includes information about the operating status of each control link. By real-time monitoring of the constant current source's output current, the stepper motor's position feedback, and the temperature sensor's value, the control unit ensures that the LED lamp provides stable and reliable lighting output under various environmental conditions.

[0079] S105. Determine whether the beam angle needs to be adjusted by matching the beam angle with the environmental perception data. If so, change the lens position of the LED lamp and determine the output spectrum characteristics of the LED lamp by measuring with a spectrum analyzer.

[0080] A data matching analysis is performed based on the beam angle parameters in the complete output control execution state of the LED lamp and the standardized environmental perception data set. The degree of adaptation of the current beam angle to the ambient light intensity and personnel flow density is evaluated through numerical correlation calculation. If the correlation between the beam angle parameters and the environmental perception data is lower than a preset matching threshold, it is determined that the beam angle needs to be adjusted, and an angle adjustment command signal is obtained. The angle adjustment command signal is used to drive the motor control module built into the LED lamp, and a lens movement command is sent to the mechanical adjustment device through a position control method. The mechanical adjustment device is driven to change the spatial position of the lens assembly inside the LED lamp according to the angle adjustment amount, thereby obtaining the position-adjusted LED lamp beam configuration. Based on the position-adjusted LED lamp beam configuration, a spectrum analyzer is used to measure the spectral distribution of the actual output beam of the LED lamp. A luminous flux measurement value is obtained through luminous flux integral calculation based on the spectral measurement data. At the same time, a color rendering index measurement value is obtained through color rendering index calculation based on the spectral distribution data, thereby obtaining the spectral characteristic measurement data of the LED lamp. The data recording method is used to store parameters according to the luminous flux measurement value and the color rendering index measurement value in the LED lamp spectral characteristic measurement data. The luminous flux measurement value is compared with the preset luminous flux reference value through data comparison, and the color rendering index measurement value is compared with the preset color rendering reference value to obtain the LED lamp spectral characteristic verification result.

[0081] Exemplarily, the numerical correlation calculation is based on the mathematical correlation between the beam angle parameters and the environmental perception data for adaptation evaluation. When the current beam angle of the LED lamp is set to 60 degrees, this angle can cover a lighting area with a radius of 3 meters in a standard office environment. The suitability of the current beam angle is judged by calculating the numerical correlation by analyzing the ambient light intensity and personnel flow density data. When the ambient light intensity is 200 lux and the personnel flow density is 1.5 people per square meter, the calculated correlation is 0.75. The preset matching threshold is usually set to 0.8. A threshold below this threshold indicates that the current beam angle does not match the environmental requirements.

[0082] In one possible implementation, the adaptation assessment considers the degree of overlap between the illumination coverage and the occupant distribution area. When occupants are primarily concentrated within 2 meters of the LED light, a 60-degree beam angle creates excessive light scattering, wasting energy outside the effective illumination area. This mismatch is then used to generate the angle adjustment command signal, providing a precise basis for subsequent mechanical adjustments.

[0083] It's important to note that the LED light's built-in motor control module uses microstepping technology to precisely adjust the lens position. After receiving the angle adjustment command signal, the module converts the angle change into motor rotation steps, with each step corresponding to an angle adjustment precision of 0.1 degrees. The position control method utilizes a closed-loop feedback mechanism, with a position sensor monitoring the actual position of the lens assembly in real time to ensure accurate adjustment. When adjusting the beam angle from 60 degrees to 45 degrees, the motor control module calculates that 150 microstepping movements are required.

[0084] In one embodiment, a mechanical adjustment device converts the motor's rotational motion into linear movement of the lens assembly via a gear transmission mechanism. The distance the lens assembly moves on the optical rail directly affects the degree of light beam convergence. When the lens moves 2 mm toward the LED chip, the beam angle shrinks by 15 degrees. This position adjustment not only changes the LED light beam configuration but also optimizes the light energy distribution density, making the lighting effect more suitable for the environment.

[0085] Specifically, a spectrum analyzer uses a beam splitter or grating element to decompose the composite light output by an LED lamp into monochromatic spectra of different wavelengths. This device measures the intensity distribution of each wavelength in the visible light range from 380 nanometers to 780 nanometers, generating a detailed spectral distribution curve. Luminous flux integral calculations are based on the spectral distribution data, calculating the total luminous flux value by weighted integration of the light intensity at each wavelength and a function of human visual sensitivity. When an LED lamp operates at a color temperature of 4000K, a typical luminous flux measurement is approximately 1200 lumens.

[0086] For example, the CRI calculation process involves evaluating the color reproduction capabilities of standard color samples under LED light sources. This calculation uses eight standard color samples and determines the CRI by comparing the color differences between these samples under LED and standard light sources. The measured data on LED lamp spectral characteristics not only includes luminous flux and CRI values, but also records the stability of the spectral distribution and color temperature consistency, providing a comprehensive data foundation for the evaluation of lighting quality.

[0087] In one possible implementation, a data logging method stores measured spectral characteristic parameters in a time series to establish a historical database of LED lamp performance. This database can track performance trends under different operating conditions, providing data support for preventive maintenance. Statistical analysis is used to calculate the deviation between the measured values and the preset baseline values. When the measured luminous flux value is 1150 lumens and the preset luminous flux baseline value is 1200 lumens, the deviation rate is 4.2%.

[0088] It should be noted that the preset luminous flux and color rendering benchmark values are determined based on lighting standards and application scenario requirements. For office environments, the luminous flux benchmark is typically set at an illumination requirement of 300 to 500 lux per square meter, and the color rendering benchmark is set at a high color rendering requirement of Ra greater than 80. The LED lamp spectral characteristic verification results, through a binary pass / fail judgment, provide performance feedback information for intelligent lighting control, ensuring that lighting quality remains at the expected level.

[0089] S106 , determining the sensor measurement deviation by comparing the LED lamp output spectral characteristic data with the preset standard spectral data, performing compensation calculation on the sensor measurement deviation, and updating the sensor calibration parameters if the calibration deviation cumulative value exceeds the preset error range.

[0090] A wavelength-by-wavelength difference calculation is performed based on the measured LED light spectral characteristic data and pre-stored standard spectral data using a wavelength-corresponding comparison method. The light intensity deviations at each wavelength are numerically accumulated using difference statistical processing. The wavelength deviations are then aggregated using a summation calculation to obtain the overall sensor measurement deviation. A linear compensation algorithm is then used to perform deviation correction calculation using the overall sensor measurement deviation. A compensation coefficient and compensation direction are determined based on the numerical characteristics of the measurement deviation. The overall sensor measurement deviation is then converted into a sensor output correction coefficient through numerical calculation to obtain the sensor deviation correction coefficient. Based on the sensor deviation correction coefficient, a historical data accumulation method is used to numerically superimpose the current deviation correction coefficient with historical deviation records. The accumulated deviation correction coefficient value is then numerically compared with a preset error control range using a threshold comparison. If the accumulated deviation correction coefficient value exceeds the preset error control range, it is determined that the sensor calibration parameters need to be reset, and a sensor parameter reset instruction is generated. The sensor parameter reset instruction uses the least squares method to perform a linear fitting calculation on the sensor gain coefficient. A gain coefficient adjustment value is calculated based on the numerical characteristics of the accumulated deviation correction coefficient value. An offset adjustment value is calculated based on a deviation baseline reference value to obtain updated sensor calibration parameter data.

[0091] Exemplarily, the wavelength correspondence comparison method identifies deviations based on the precise wavelength matching of spectral data. The LED lamp spectral characteristic measurement data covers the visible light range of 380 nanometers to 780 nanometers, with each wavelength interval of 1 nanometer, and contains a total of 400 data points. The pre-stored standard spectral data comes from a calibrated standard light source and represents the ideal spectral distribution characteristics. When the measured data shows that the light intensity at a wavelength of 520 nanometers is 85%, and the standard data shows that the wavelength should be 90%, the difference between the two is 5%. The difference statistical processing performs similar calculations by traversing all 400 wavelength points and recording the deviation value of each wavelength.

[0092] In one possible implementation, a numerical accumulation operation weights the deviation values of each wavelength according to their contribution to the total spectrum. Due to the higher sensitivity of the human eye, the deviation value weighting factor for the green wavelength region is set to 1.2, while the weighting factors for the red and blue wavelength regions are set to 0.8. This summation process accounts for the mutual cancellation effect of positive and negative deviations. When some wavelength deviations are positive and some are negative, the overall measured deviation value reflects the systematic error trend of the sensor. This comprehensive deviation calculation provides a quantitative basis for subsequent correction.

[0093] It should be noted that the linear compensation algorithm corrects for deviations based on the assumption of linear sensor response characteristics. This algorithm assumes that the sensor's measurement error varies linearly within a certain range and performs correction by establishing a linear relationship between input light intensity and output signal. A negative 3% overall sensor measurement deviation indicates an under-response and requires positive compensation. The compensation coefficient is calculated based on the absolute magnitude and direction of the deviation; a negative 3% deviation corresponds to a positive compensation coefficient of 1.03.

[0094] In one embodiment, a numerical calculation process converts abstract deviation values into directly applicable correction parameters. The sensor deviation correction coefficient not only includes amplitude correction information but also includes parameters for spectral response correction. This coefficient acts as a multiplication factor in the sensor's signal processing chain, directly applying it to the raw measurement data to achieve real-time deviation compensation. This dynamic correction mechanism ensures stable measurement accuracy over long-term sensor use.

[0095] Specifically, the historical data accumulation method tracks long-term trends in sensor performance through time series analysis. This method maintains a sliding window database that stores the most recent 30 records of bias correction coefficients and calculates the cumulative trend of the deviations through a moving average calculation. When multiple consecutive measurements show that the bias correction coefficient fluctuates between 1.02 and 1.04, the cumulative value calculation indicates that the sensor has persistent systematic deviations. A threshold comparison mechanism triggers a parameter reset procedure when the cumulative deviation exceeds 5%. This threshold is determined based on the sensor's accuracy requirements and the tolerance range of the application scenario.

[0096] For example, the setting of the preset error control range takes into account the actual requirements of lighting applications for spectral accuracy. In high-precision color rendering applications, the error control range is set to 2%, while in general lighting applications it can be relaxed to 5%. The generation of sensor parameter reset instructions is based on risk assessment. When the cumulative deviation approaches the upper limit of the control range, the system actively starts the calibration procedure to avoid the degradation of lighting quality due to excessive deviation. Least squares fitting and parameter update calculation In one possible implementation, the least squares method optimizes the gain coefficient by establishing a mathematical model of the sensor response curve. This method uses historical measurement data and corresponding standard reference values as training samples, and determines the optimal gain coefficient by minimizing the square error between the predicted value and the actual value. When analyzing 20 sets of historical data and finding that the sensor response has a linear coefficient of 0.98, the least squares method calculates that the gain coefficient needs to be adjusted to 1.02 to compensate for this deviation.

[0097] It should be noted that the offset adjustment calculation is based on the analysis of the sensor's zero drift. The offset reference value represents the expected sensor output level at zero input. When the sensor detects a weak signal output in the absence of light, the offset adjustment is used to eliminate this zero drift. The sensor's updated calibration parameter data, including the new gain factor and offset values, is written to the sensor's non-volatile memory to ensure the durability of the calibration results and parameter retention across reboots.

[0098] S107 , after updating the sensor calibration parameters, collecting the LED lamp power consumption data, and if the deviation between the actual power consumption and the theoretical calculated value is less than a preset percentage, it is determined that the energy-saving control effect has achieved the expected goal.

[0099] According to the updated calibration parameter data of the sensor, a data writing method is used to store the new gain coefficient and offset into the internal memory of the sensor. The calibration parameters are verified by data reading to confirm that the correct update to the sensor working configuration has been made. At the same time, the working voltage and current of the LED lamp are continuously measured by the energy consumption monitoring module to obtain the current power consumption data of the LED lamp. The actual power consumption value is obtained by using the electric power calculation formula using the current power consumption data of the LED lamp. The theoretical power consumption value of the LED lamp is calculated using the power consumption estimation formula according to the current brightness setting value, color temperature configuration and beam angle parameters of the LED lamp. The actual power consumption value and the theoretical power consumption value are synchronously recorded through data comparison to obtain a power consumption value comparison record. According to the power consumption value comparison record, a numerical subtraction method is used to obtain the difference between the actual power consumption and the theoretical power consumption. The ratio of the difference to the theoretical power consumption value is converted into a power consumption deviation percentage through proportional calculation. If the power consumption deviation percentage is less than the preset energy-saving deviation limit, it is judged that the power consumption control of the LED lamp meets the energy-saving requirements, and the energy-saving control compliance judgment result is obtained. The result evaluation method is used to finally confirm the overall execution effect of the intelligent lighting energy-saving control through the energy-saving control standard judgment result. According to the passing status of the energy-saving control standard judgment, it is determined that the energy-saving control effect has reached the expected goal. The completion signal generator is used to output the execution completion status of the energy-saving control process to obtain the energy-saving control system operation completion signal.

[0100] For example, the data writing method transfers calibration parameters to the sensor's EEPROM memory via the I2C communication protocol. This process involves precisely writing two key parameters: the gain factor and the offset. When the gain factor needs to be adjusted from 1.00 to 1.03, the system converts this value into 16-bit binary data and writes it to a specific memory address. Data read verification ensures the success of the write operation through a readback mechanism. The sensor controller reads the newly written data and compares it with the original data to verify data integrity and accuracy. This dual confirmation mechanism eliminates the risk of calibration failure due to communication interference or storage failure.

[0101] In one possible implementation, the energy consumption monitoring module uses a high-precision current sensor and voltage sampling circuit to track the LED lamp's power consumption in real time. This module uses a Hall-effect current sensor to monitor the operating current of the LED driver circuit, while a voltage divider circuit samples the LED lamp's operating voltage. When the LED lamp operates at 75% brightness, the current sensor measures a current of 280 mA, while the voltage sampled voltage indicates a voltage of 12.5 V. The product of these two values yields the current power consumption data, 3.5 watts. This continuous measurement ensures the timeliness and accuracy of power consumption data.

[0102] It should be noted that the electric power calculation formula is based on the fundamental principles of Ohm's law, multiplying the voltage and current to obtain the actual power consumption value. This calculation takes into account the efficiency loss of the LED driver circuit and the influence of temperature. When the ambient temperature is 25 degrees Celsius, the luminous efficiency of the LED chip is 110 lumens per watt. The power consumption estimation formula is theoretically calculated based on the design parameters and current operating status of the LED lamp. This formula comprehensively considers the linear effect of the brightness setting value on power consumption, the influence of color temperature configuration on the power consumption distribution of different color LED chips, and the effect of beam angle adjustment on the power consumption of optical components.

[0103] P total =P base The formula (α·B+β·f(CT)+γ·g(θ)) represents the total power consumption calculation model for LED lamps, where P_total represents the total power consumption, P_base represents the base power consumption, B represents the brightness setting value (0-100%), CT represents the color temperature value, and θ represents the beam angle. α, β, and γ are the weight coefficients of brightness, color temperature, and beam angle, respectively. f(CT) is the function that affects color temperature on power consumption, and g(θ) is the function that affects beam angle on power consumption.

[0104]

[0105] This formula is the function of the influence of color temperature on power consumption, where f(CT) represents the power consumption coefficient at a specific color temperature, CT represents the current color temperature value, CT_min and CT_max represent the minimum and maximum color temperature values respectively, P_warm represents the power consumption coefficient of the warm light LED chip, and P_cool represents the power consumption coefficient of the cold light LED chip.

[0106] The formula realizes the linear distribution of the power of the cold and warm light chips at different color temperatures.

[0107] This formula is a function of the effect of beam angle on power consumption, where g(θ) represents the power consumption coefficient at a specific beam angle, θ represents the current beam angle, θ_min and θ_max represent the minimum and maximum beam angles, respectively, and k is the optical adjustment factor. As the beam angle decreases, more optical focusing components are typically required, resulting in additional power consumption.

[0108] In one embodiment, the theoretical power consumption calculation process reflects the complexity of multi-parameter coupling. When the LED lamp is set to 4000K color temperature, 80% brightness, and a 45-degree beam angle, the theoretical calculation needs to consider the power consumption distribution of the red, green, and blue LED chips separately. The 4000K color temperature corresponds to a red chip ratio of 35%, a green chip ratio of 40%, and a blue chip ratio of 25%. 80% brightness corresponds to a drive current of 80% of the rated current for each chip. The 45-degree beam angle corresponds to an additional power consumption of 0.2 watts by the lens adjustment mechanism. Data comparison ensures the temporal consistency of actual and theoretical values through synchronous recording.

[0109] Specifically, the numerical subtraction method uses absolute difference calculations to reveal the degree of deviation between actual power consumption and theoretical expectations. This calculation not only considers the magnitude of the numerical value but also analyzes the direction of the deviation. A positive deviation indicates that the actual power consumption exceeds the theoretical value, while a negative deviation indicates that the energy savings exceed expectations. A proportional calculation converts the absolute difference into a relative percentage, eliminating the differences between LED lights of different power levels. When the actual power consumption is 3.5 watts and the theoretical power consumption is 3.3 watts, the power consumption deviation percentage is 6.1%.

[0110] For example, the preset energy-saving deviation limit is set based on a balance between energy efficiency standards and application requirements. This limit is typically set between 5% and 10%, ensuring stringent energy-saving control while also allowing for a reasonable margin of error in practical applications. This threshold comparison provides a clear pass / fail judgment, providing a quantitative basis for subsequent control optimization. This data-based judgment avoids the uncertainty of subjective assessments.

[0111] In one possible implementation, the results evaluation method comprehensively considers performance across three dimensions: energy efficiency, lighting quality, and control accuracy. This method not only focuses on achieving power consumption targets but also verifies the coordination and stability of the entire intelligent lighting control process. Achieving these goals effectively validates the complete closed-loop system from environmental perception, demand prediction, parameter optimization, to execution and control.

[0112] It should be noted that the completion status output by the completion signal generator represents the successful completion of the entire energy-saving control cycle. This signal not only confirms the completion of the current control cycle but also provides the trigger condition for the initiation of the next control cycle. The generation of the energy-saving control system completion signal indicates that the intelligent lighting system has achieved the optimal energy efficiency configuration under the current environmental conditions, laying the foundation for continued energy-saving operation.

[0113] The present invention provides an intelligent energy-saving control system for LED lamps, which mainly includes:

[0114] The data preprocessing module is used to obtain the ambient light intensity data, personnel flow density data and current working status parameters of LED lights collected by multiple sensors, preprocess the raw sensor data, and obtain a standardized environmental perception data set;

[0115] The trend prediction module is used to predict the trend of crowd density changes based on historical crowd flow density data. If the crowd density shows an upward trend within a continuous preset time window, the crowd density change trend is input into the dynamic lighting demand prediction model established based on the standardized environmental perception data set, and the output is the lighting demand increment coefficient;

[0116] The lighting demand prediction module is used to determine the target brightness, color temperature, and beam angle parameters of each LED lamp node through the lighting demand increment coefficient. If the difference between the target brightness value and the current brightness value exceeds a preset percentage, the color temperature and beam angle parameters are adjusted to generate an LED lamp control parameter optimization plan;

[0117] The parameter optimization module is used to evaluate the computing power resources required for the intelligent control system based on the LED light control parameter optimization plan, control the LED light brightness output based on the computing power resources, and adjust the current ratio of the red, green and blue LED chips according to the color temperature;

[0118] The computing power control module is used to determine whether the beam angle needs to be adjusted by matching the beam angle with the environmental perception data. If so, the lens position of the LED lamp is changed and the output spectrum characteristics of the LED lamp are measured by the spectrum analyzer.

[0119] The beam adjustment module is used to determine the sensor measurement deviation by comparing the spectral characteristic data of the LED light output with the preset standard spectral data, and to compensate for the sensor measurement deviation. If the accumulated value of the calibration deviation exceeds the preset error range, the sensor calibration parameters are updated;

[0120] The calibration evaluation module is used to collect the power consumption data of the LED lamp after updating the sensor calibration parameters. If the deviation between the actual power consumption and the theoretical calculated value is less than a preset percentage, it is determined that the energy-saving control effect has achieved the expected goal.

[0121] The above description is only a specific implementation method of this specification. Those skilled in the art will clearly understand that for the convenience and brevity of description, the specific working processes of the systems, modules and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here. It should be understood that the scope of protection of this specification is not limited to this. Any person skilled in the art can easily think of various equivalent modifications or replacements within the technical scope disclosed in this specification, and these modifications or replacements should be included in the scope of protection of this specification.

Claims

1. An intelligent energy-saving control method for LED lamps, characterized in that: The method comprises: Obtain ambient light intensity data, personnel flow density data, and LED light operating status parameters collected by multiple sensors to generate a standardized environmental perception data set; Predicting a change trend of personnel flow density based on historical personnel flow density data in the standardized environmental perception data set, and generating a lighting demand increment coefficient; Determine the target brightness parameter, target color temperature parameter and target beam angle parameter of each LED lamp node according to the light demand increment coefficient, and generate an LED lamp control parameter set; Controlling the brightness output of the LED lamp through pulse width modulation according to the brightness adjustment value in the LED lamp control parameter set, adjusting the current ratio of the red, green and blue LED chips according to the color temperature ratio value in the LED lamp control parameter set, and driving the mechanical adjustment device to change the position of the LED lamp lens according to the beam angle adjustment amount in the LED lamp control parameter set; Measure the luminous flux and color rendering index of LED lights through a spectrum analyzer to generate spectral characteristic data of LED lights; Generating sensor calibration parameters based on the comparison of the LED light spectrum characteristic data with preset standard spectrum data; The sensor configuration is adjusted according to the sensor calibration parameters, the power consumption data of the LED lamp is collected through the energy consumption monitoring module, and the energy-saving control execution status result is generated.

2. The LED lamp intelligent energy-saving control method according to claim 1, characterized in that: The method of acquiring ambient light intensity data, personnel flow density data, and LED light operating status parameters collected by multiple sensors to generate a standardized environmental perception data set includes: Obtaining the original value of ambient light intensity, the original value of personnel flow density, and the original parameter of the working state of the LED lamp through a multi-channel sensor interface, performing outlier detection and noise filtering on the original value of ambient light intensity, the original value of personnel flow density, and the original parameter of the working state of the LED lamp using a data cleaning method, and generating a first light intensity value, a first flow density value, and a first working state parameter; Generate a second light intensity value by using a minimum-maximum normalization method based on the first light intensity value, generate a second flow density value by using a Z-score normalization method based on the first flow density value, and generate a second working state parameter by using a one-hot encoding method based on the first working state parameter; Using a time synchronization alignment method to match the second light intensity value and the second flow density value in a time dimension, and using a frequency synchronization method to align the second working state parameter with the matched data in a frequency dimension to generate synchronized multidimensional data; The synchronized multidimensional data are merged according to preset weighting coefficients using a data fusion method to generate a standardized environmental perception data set.

3. The LED lamp intelligent energy-saving control method according to claim 1, characterized in that: The method of predicting a change trend of personnel flow density based on historical personnel flow density data in the standardized environmental perception data set and generating a lighting demand increment coefficient includes: Generate a personnel flow density prediction sequence using a time series analysis method based on the historical personnel flow density data in the standardized environmental perception data set, perform trend analysis on the personnel flow density prediction sequence using a sliding window mechanism, and generate a flow density change trend parameter; Generate a dynamic lighting demand prediction model by using a linear regression method based on the correlation matching between the traffic density change trend parameter and the light intensity historical data in the standardized environment perception data set; The dynamic lighting demand prediction model uses the traffic density change trend parameter as input to generate a lighting demand adjustment value; The illumination requirement adjustment value is converted into an illumination requirement increment coefficient by adopting a normalization processing method.

4. The LED lamp intelligent energy-saving control method according to claim 1, characterized in that: The step of determining the target brightness parameter, target color temperature parameter, and target beam angle parameter of each LED lamp node according to the illumination demand increment coefficient and generating a set of LED lamp control parameters includes: According to the illumination demand increment coefficient, a proportional calculation method is used to generate a target brightness parameter for each LED lamp node, and an interpolation calculation method is used to generate a target color temperature parameter and a target beam angle parameter, thereby generating a target parameter set for the LED lamp node; The brightness adjustment value, color temperature ratio value and beam angle adjustment amount are generated according to the LED lamp node target parameter set, and the LED lamp control parameter set is generated by adopting a data encapsulation method.

5. The LED lamp intelligent energy-saving control method according to claim 4, characterized in that: The method of generating a brightness adjustment value, a color temperature ratio value, and a beam angle adjustment amount according to the target parameter set of the LED lamp node, and generating an LED lamp control parameter set using a data encapsulation method, includes: Calculate the brightness adjustment value according to the target brightness parameter in the target parameter set of the LED lamp node and the current brightness value, convert the target color temperature parameter in the target parameter set of the LED lamp node into a color temperature ratio value using a color temperature conversion method, and convert the target beam angle parameter in the target parameter set of the LED lamp node into a beam angle adjustment value using an angle difference calculation; The brightness adjustment value, color temperature ratio value and beam angle adjustment amount are converted into an instruction format recognized by an LED lamp driver by using a data encapsulation method to generate an LED lamp control parameter set.

6. The LED lamp intelligent energy-saving control method according to claim 5, characterized in that: The controlling the brightness output of the LED lamp by pulse width modulation according to the brightness adjustment value in the LED lamp control parameter set, and adjusting the current ratio of the red, green and blue LED chips according to the color temperature ratio value in the LED lamp control parameter set, includes: Generate an LED lamp brightness modulation signal sequence through a pulse width modulation signal generator according to the brightness adjustment value in the LED lamp control parameter set; According to the color temperature ratio value in the LED lamp control parameter set, a three-way current controller is used to generate the driving current distribution parameters of the red, green and blue LED chips, and a constant current source circuit is used to current drive the red, green and blue LED chips.

7. The LED lamp intelligent energy-saving control method according to claim 1, characterized in that: The method of measuring the luminous flux value and color rendering index value of the LED lamp by using a spectrum analyzer to generate the spectral characteristic data of the LED lamp includes: Measuring the spectral distribution of the output light beam of the LED lamp under the control of the LED lamp control parameter set by a spectrum analyzer to generate a luminous flux value and a color rendering index value; The spectral characteristic data of the LED lamp is generated by adopting a data recording method according to the luminous flux value and the color rendering index value.

8. The LED lamp intelligent energy-saving control method according to claim 1, characterized in that: The generating of sensor calibration parameters based on the comparison of the LED lamp spectral characteristic data with preset standard spectral data includes: Generating a sensor measurement deviation value by using a wavelength corresponding comparison method based on the LED lamp spectral characteristic data and preset standard spectral data; A linear compensation algorithm is used to generate sensor calibration parameters according to the sensor measurement deviation value.

9. The LED lamp intelligent energy-saving control method according to claim 8, characterized in that: The adjusting the sensor configuration according to the sensor calibration parameters, collecting the LED lamp power consumption data through the energy consumption monitoring module, and generating the energy-saving control execution status result includes: Adjusting the sensor configuration using a data writing method according to the sensor calibration parameters; The power consumption data of the LED lamp is collected through the energy consumption monitoring module, and the energy-saving control execution status result is generated using the electric power calculation formula.

10. An intelligent energy-saving control system for LED lamps, characterized in that: The system comprises: The data preprocessing module is used to obtain the ambient light intensity data, personnel flow density data and current working status parameters of LED lights collected by multiple sensors, preprocess the raw sensor data, and obtain a standardized environmental perception data set; The trend prediction module is used to predict the trend of crowd density changes based on historical crowd flow density data. If the crowd density shows an upward trend within a continuous preset time window, the crowd density change trend is input into the dynamic lighting demand prediction model established based on the standardized environmental perception data set, and the output is the lighting demand increment coefficient; The lighting demand prediction module is used to determine the target brightness, color temperature, and beam angle parameters of each LED lamp node through the lighting demand increment coefficient. If the difference between the target brightness value and the current brightness value exceeds a preset percentage, the color temperature and beam angle parameters are adjusted to generate an LED lamp control parameter optimization plan; The parameter optimization module is used to evaluate the computing power resources required for the intelligent control system based on the LED light control parameter optimization plan, control the LED light brightness output based on the computing power resources, and adjust the current ratio of the red, green and blue LED chips according to the color temperature; The computing power control module is used to determine whether the beam angle needs to be adjusted by matching the beam angle with the environmental perception data. If so, the lens position of the LED lamp is changed and the output spectrum characteristics of the LED lamp are measured by the spectrum analyzer. The beam adjustment module is used to determine the sensor measurement deviation by comparing the spectral characteristic data of the LED light output with the preset standard spectral data, and to compensate for the sensor measurement deviation. If the accumulated value of the calibration deviation exceeds the preset error range, the sensor calibration parameters are updated; The calibration evaluation module is used to collect the power consumption data of the LED lamp after updating the sensor calibration parameters. If the deviation between the actual power consumption and the theoretical calculated value is less than a preset percentage, it is determined that the energy-saving control effect has achieved the expected goal.

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