Solar energy LED street lamp multi-color temperature self-adaptive adjusting system and method based on power generation

Abstract

The application relates to the technical field of energy-saving lighting, and discloses a solar LED street lamp multi-color-temperature self-adaptive adjustment system and method based on power generation, which comprises a data acquisition and monitoring module, a power generation potential prediction module, an illumination demand analysis module, a multi-color-temperature decision module, a light source driving control module, a dynamic sensing adjustment module and an efficiency evaluation optimization module; the method predicts future available power generation by collecting real-time meteorological data and illumination intensity data, determines road lighting priorities in combination with preset crowd density data, matches the best multi-color-temperature combination scheme in a color temperature and energy consumption relationship database, and realizes 2700K to 5700K continuous color temperature output by dynamically adjusting the current duty cycle of different color temperature LED chips by using pulse width modulation technology. The application improves the visual comfort and road lighting experience of drivers, and guarantees the balance between lighting quality and energy utilization efficiency.

Description

Technical Field

[0001] This invention relates to the field of energy-saving lighting technology, specifically to a multi-color temperature adaptive adjustment system and method for solar LED streetlights based on power generation. Background Technology

[0002] Energy-saving lighting is a new type of light source based on LED technology, which features energy saving and long service life.

[0003] Currently, in the practical application of solar LED streetlights, due to the fluctuations in solar power generation caused by weather conditions, existing control systems cannot accurately predict the available power generation in future periods. They often rely solely on the current battery charge for simple switching and single-color temperature dimming control, resulting in frequent insufficient lighting brightness and sudden light outages on cloudy or rainy days and in winter when there is insufficient sunlight, affecting road traffic safety.

[0004] Therefore, a multi-color temperature adaptive adjustment system and method for solar LED streetlights based on power generation is proposed to solve the above problems. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a multi-color temperature adaptive adjustment system and method for solar LED streetlights based on power generation, which solves the problem mentioned in the background that existing control systems are unable to accurately predict the available power generation in future periods, and that this affects road traffic safety.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a multi-color temperature adaptive adjustment system and method for solar LED streetlights based on power generation, the system comprising:

[0007] The data acquisition and monitoring module includes a meteorological sensor unit, a light sensor unit, and a power generation detection unit, which collect environmental parameters and power generation system operation data;

[0008] The power generation potential prediction module, connected to the data acquisition and monitoring module, includes a historical data analysis unit, a meteorological correlation unit, and a power generation prediction unit, which generates predicted data on available power generation for future periods.

[0009] The lighting demand analysis module receives the output data from the power generation potential prediction module, including a pedestrian flow statistics unit, a road classification unit, and a demand priority determination unit, to determine the lighting level configuration for different road sections.

[0010] The multi-color temperature decision module, coupled with the power generation potential prediction module and the lighting demand analysis module, includes a color temperature and energy consumption mapping unit, a light mixing scheme generation unit and a comfort optimization unit, to generate a target color temperature configuration that is adapted to the current energy conditions.

[0011] The light source drive control module, connected to the multi-color temperature decision module, includes a multi-channel PWM control unit, a constant current drive unit, and a spectral feedback unit, and performs accurate output adjustment of the multi-color temperature LED;

[0012] The dynamic sensing and adjustment module forms a closed loop with the light source driving control module, including a motion detection unit, an ambient light sensing unit, and a local dimming unit;

[0013] The performance evaluation and optimization module integrates the operational data from various modules, including the energy efficiency metering unit, the lighting quality assessment unit, and the strategy self-learning unit, to continuously optimize the system adjustment strategy.

[0014] Preferably, the method includes:

[0015] S1. Collect real-time meteorological data and light intensity data of the environment where the solar LED street light is located, and simultaneously collect the output voltage and current data of the solar panel to generate environmental monitoring dataset and power generation monitoring dataset.

[0016] S2. Based on the environmental monitoring dataset and the power generation monitoring dataset, perform solar power generation potential assessment and processing, combine the historical power generation database to predict the available power generation in future periods, and generate power generation prediction data.

[0017] S3. Based on the power generation prediction data and environmental monitoring dataset, perform lighting demand level classification processing, combine preset pedestrian flow density data to determine road lighting priority, and generate lighting demand level data.

[0018] S4. Based on the power generation prediction data and lighting demand classification data, perform multi-color temperature combination scheme matching processing, match the preset color temperature and energy consumption relationship library to generate an appropriate color temperature configuration scheme, and generate target color temperature configuration data.

[0019] S5. Drive the LED light source module to perform multi-color temperature mixed output adjustment according to the target color temperature configuration data, and control the current duty cycle of LED chips with different color temperatures through pulse width modulation technology to generate actual color temperature output data.

[0020] S6. Based on actual color temperature output data, monitor the dynamic distribution of vehicles and pedestrians in the lighting area in real time, and trigger dynamic fine-tuning of color temperature in combination with changes in ambient light intensity to generate dynamic adjustment feedback data.

[0021] S7. Integrate the power generation prediction data, actual color temperature output data and dynamic adjustment feedback data to construct an efficiency evaluation dataset. Optimize the system using adaptive strategies based on the dual indicators of energy efficiency and lighting quality to generate optimized system configuration data.

[0022] Preferably, the steps in S1 of collecting real-time meteorological data and light intensity data of the environment where the solar LED street light is located, and simultaneously collecting output voltage and current data of the solar panel, to generate an environmental monitoring dataset and a power generation monitoring dataset include the following:

[0023] S11. Collect temperature, humidity, and wind speed data through meteorological sensors deployed on the top of the light pole, and collect vertical and horizontal illuminance data through light sensors to construct an environmental monitoring dataset containing timestamps.

[0024] S12. Collect real-time power generation data by connecting the voltage transformer and current sensor in series in the output circuit of the solar panel, record the working status parameters of the charge and discharge controller, and construct a power generation monitoring dataset that includes instantaneous power generation and cumulative power generation.

[0025] Among them, real-time power generation data Calculated using the following formula:

[0026] ;

[0027] in, for Real-time output power of the solar panels. for The output voltage of the solar panel at any given time. for The output current of the solar panel at any given time;

[0028] S13. Upload the environmental monitoring dataset and the power generation monitoring dataset to the cloud management platform for classified storage, and establish a data index table with time-series labels.

[0029] Preferably, step S2 involves the following steps: assessing the solar power generation potential based on the environmental monitoring dataset and the power generation monitoring dataset, and combining this with a historical power generation database to predict available power generation for future periods, thereby generating power generation prediction data:

[0030] S21. Extract cloud coverage and solar radiation intensity data from the environmental monitoring dataset, and combine them with the weather forecast API to obtain meteorological forecast data for the next 24 hours.

[0031] S22. Call the power generation statistical model of the same season and weather pattern in the historical power generation database, and use the time series analysis method to calculate the expected power generation curve for future periods.

[0032] S23. Based on the current state of charge of the battery and the expected charging efficiency, calculate the net available power generation for nighttime lighting and generate power generation prediction data with confidence intervals.

[0033] Preferably, step S3 involves classifying lighting demand levels based on the predicted power generation data and the environmental monitoring dataset, determining road lighting priorities by combining preset pedestrian density data, and generating lighting demand grading data, including the following steps:

[0034] S31. Obtain road traffic flow statistics by time period, divide lighting time into peak time, off-peak time and low-peak time, and set the benchmark illuminance threshold for each time period;

[0035] S32. Based on the net available power generation value in the power generation prediction data, classify the power supply into three levels: surplus power supply mode, balanced power supply mode and energy-saving operation mode.

[0036] S33. Perform matrix matching between power supply level and lighting time period category to generate lighting demand classification data including priority protection sections and adjustable section lighting demand classification data.

[0037] Preferably, in step S4, the multi-color temperature combination scheme matching process is performed based on the power generation prediction data and the lighting demand classification data. This process, which matches the preset color temperature and energy consumption relationship database to generate a suitable color temperature configuration scheme and generates target color temperature configuration data, includes the following steps:

[0038] S41. Pre-store the power consumption characteristic curves of LED combinations with different color temperatures, and establish a three-primary-color mixing energy consumption mapping table for 3000K warm white light, 4000K neutral light, and 5000K cool white light.

[0039] S42. Based on the power supply level in the lighting demand classification data, retrieve color temperature combination schemes that meet the illuminance standard and have energy consumption lower than the available power generation in the color temperature and energy consumption relationship database.

[0040] Among them, the total energy consumption of color temperature combination Evaluate using the following formula:

[0041] ;

[0042] in, The total energy consumption of the selected color temperature combination within a set time period. The total number of color temperature channels participating in light mixing. For the first The power of each color temperature channel LED at its rated brightness. For the first The planned activation duration for each color temperature channel. For the first Dimming duty cycle of each color temperature channel;

[0043] S43. Based on human comfort research data, prioritize the color temperature configuration that conforms to the circadian rhythm and generate target color temperature configuration data that includes the brightness ratio of each color temperature channel.

[0044] Preferably, in step S5, the LED light source module is driven to perform multi-color temperature mixing output adjustment according to the target color temperature configuration data, and the current duty cycle of LED chips with different color temperatures is controlled by pulse width modulation technology to generate actual color temperature output data, including the following steps:

[0045] S51. Analyze the target brightness values ​​of each color temperature channel in the target color temperature configuration data and convert them into the pulse width modulation duty cycle control signal of the corresponding LED driver;

[0046] S52. Adjust the on-time ratio of the three groups of LED chips (warm white, neutral white, and cool white) through a constant current drive circuit.

[0047] S53. Real-time acquisition of LED junction temperature and luminous flux output data, maintaining color temperature stability through closed-loop feedback control, and generating color temperature verification data corresponding to the actual output spectrum.

[0048] Preferably, in step S6, based on actual color temperature output data, the dynamic distribution of vehicles and pedestrians within the illuminated area is monitored in real time, and dynamic adjustment of color temperature is triggered by changes in ambient light intensity to generate dynamic adjustment feedback data, including the following steps:

[0049] S61. Use radar detectors and infrared thermal imagers to monitor the number, speed and distribution density of moving objects within the illuminated area;

[0050] S62. When a densely populated pedestrian area is detected, automatically increase the medium and high color temperature components of the projection lights in that area;

[0051] S63. When the ambient moonlight intensity exceeds the set threshold, the overall output power of the lamps is reduced and the light is shifted to a low color temperature warm light mode, generating dynamic adjustment feedback data that includes regionally differentiated adjustment parameters.

[0052] Preferably, in step S7, the integration of the predicted power generation data, actual color temperature output data, and dynamic adjustment feedback data to construct an efficiency evaluation dataset, and the generation of system optimized configuration data through adaptive strategy optimization using both energy efficiency and lighting quality indicators, includes the following steps:

[0053] S71. Calculate the actual power generation per unit time and the total power consumption of the LED system, and calculate the energy utilization rate and energy storage buffer coefficient.

[0054] S72. Collect average illuminance and uniformity data of the road surface through illuminance sensors, and quantify the lighting quality score by combining the glare index evaluation model.

[0055] S73. Establish a strategy optimization knowledge base based on historical adjustment records. When energy shortages and lighting complaints occur continuously, automatically adjust the color temperature allocation weight and dimming strategy to generate system optimization configuration data containing long-term optimization solutions.

[0056] Preferably, it also includes maintenance early warning steps:

[0057] The system collects real-time data on the output efficiency degradation rate and surface contamination of solar panels. When the power generation efficiency drops below a preset threshold, a cleaning reminder signal is generated.

[0058] The system uses rainfall probability data from the environmental monitoring dataset to trigger a remote alarm if there is no rainfall in the short term. Simultaneously, during cleaning and maintenance, it controls the LED light source module to switch to a low-power, single low color temperature output mode.

[0059] Compared with the prior art, the present invention provides a multi-color temperature adaptive adjustment system and method for solar LED streetlights based on power generation, which has the following beneficial effects:

[0060] 1. In this invention, by collecting environmental monitoring datasets and power generation monitoring datasets to assess the potential of solar power generation, power generation prediction data is generated. Combined with lighting demand classification data and road lighting priority, accurate prediction of future available power generation and reasonable allocation of lighting resources are achieved. This solves the problem of unstable lighting caused by fluctuations in solar power generation, ensures reliable road lighting under different weather conditions, and improves the stability and safety of system operation.

[0061] 2. In this invention, based on power generation prediction data and lighting demand classification data, multiple color temperature combination schemes are matched in the color temperature and energy consumption relationship database to generate suitable target color temperature configuration data. Pulse width modulation technology is used to dynamically adjust the current duty cycle of LED chips with different color temperatures, thereby maximizing the lighting quality under the constraint of limited power generation. This avoids the waste of high color temperature energy during low traffic flow periods, reduces glare interference, and improves the driver's visual comfort and road lighting experience.

[0062] 3. In this invention, by monitoring the output efficiency of the solar panel in real time and comparing it with the predicted power generation data, when the efficiency decline caused by component contamination and aging is detected, a maintenance warning is automatically triggered and the output mode of the LED light source module is adjusted in conjunction with the system. The system is switched to a low-power single low color temperature state in a timely manner, which not only prevents the battery from being damaged by excessive discharge, but also extends the overall service life of the system through adaptive strategy optimization, ensuring a balance between lighting quality and energy utilization efficiency. Attached Figure Description

[0063] Figure 1This is a schematic diagram of the architecture of a solar LED street light multi-color temperature adaptive adjustment system based on power generation according to the present invention;

[0064] Figure 2 This is a flowchart illustrating the steps of a method for adaptive adjustment of multiple color temperatures in a solar LED street light based on power generation, as described in this invention. Detailed Implementation

[0065] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0066] Please see Figures 1-2 The specific implementation of a multi-color temperature adaptive adjustment system and method for solar LED streetlights based on power generation is as follows: The system includes:

[0067] The data acquisition and monitoring module includes a meteorological sensor unit, a light sensor unit, and a power generation detection unit, which collect environmental parameters and power generation system operation data;

[0068] The power generation potential prediction module is connected to the data acquisition and monitoring module, including a historical data analysis unit, a meteorological correlation unit, and a power generation prediction unit, to generate predicted data on available power generation for future periods.

[0069] The lighting demand analysis module receives output data from the power generation potential prediction module, including a pedestrian flow statistics unit, a road classification unit, and a demand priority determination unit, to determine the lighting level configuration for different road sections.

[0070] The multi-color temperature decision module, coupled with the power generation potential prediction module and the lighting demand analysis module, includes a color temperature and energy consumption mapping unit, a light mixing scheme generation unit and a comfort optimization unit, to generate a target color temperature configuration that is adapted to the current energy conditions.

[0071] The light source drive control module is connected to the multi-color temperature decision module, including a multi-channel PWM control unit, a constant current drive unit and a spectral feedback unit, to perform accurate output adjustment of the multi-color temperature LED;

[0072] The dynamic sensing and adjustment module forms a closed loop with the light source drive control module, including a motion detection unit, an ambient light sensing unit, and a local dimming unit;

[0073] The performance evaluation and optimization module integrates the operational data from various modules, including the energy efficiency metering unit, the lighting quality assessment unit, and the strategy self-learning unit, to continuously optimize the system adjustment strategy.

[0074] The methods include:

[0075] S1. Collect real-time meteorological data and light intensity data of the environment where the solar LED street light is located, and simultaneously collect the output voltage and current data of the solar panel to generate environmental monitoring dataset and power generation monitoring dataset.

[0076] S2. Based on the environmental monitoring dataset and the power generation monitoring dataset, the potential of solar power generation is assessed and processed. Combined with the historical power generation database, the available power generation in future periods is predicted, and power generation prediction data is generated.

[0077] S3. Based on the power generation forecast data and environmental monitoring dataset, classify the lighting demand levels, combine the preset pedestrian flow density data to determine the priority of road lighting, and generate lighting demand level data.

[0078] S4. Based on the power generation forecast data and lighting demand classification data, perform multi-color temperature combination scheme matching processing, match the preset color temperature and energy consumption relationship library to generate an appropriate color temperature configuration scheme, and generate target color temperature configuration data.

[0079] S5. Drive the LED light source module to perform multi-color temperature mixed output adjustment according to the target color temperature configuration data, and control the current duty cycle of LED chips of different color temperatures through pulse width modulation technology to generate actual color temperature output data.

[0080] S6. Based on actual color temperature output data, monitor the dynamic distribution of vehicles and pedestrians in the lighting area in real time, and trigger dynamic fine-tuning of color temperature in combination with changes in ambient light intensity to generate dynamic adjustment feedback data.

[0081] S7. Integrate power generation prediction data, actual color temperature output data and dynamic adjustment feedback data to construct an efficiency evaluation dataset. Use the dual indicators of energy efficiency and lighting quality to perform adaptive strategy optimization and generate system optimization configuration data.

[0082] S1 collects real-time meteorological data and light intensity data of the environment where the solar LED streetlights are located, and simultaneously collects the output voltage and current data of the solar panels to generate environmental monitoring datasets and power generation monitoring datasets, including the following steps:

[0083] S11. Collect temperature, humidity, and wind speed data through meteorological sensors deployed on the top of the light pole, and collect vertical and horizontal illuminance data through light sensors to construct an environmental monitoring dataset containing timestamps.

[0084] S12. Collect real-time power generation data by connecting the voltage transformer and current sensor in series in the output circuit of the solar panel, record the working status parameters of the charge and discharge controller, and construct a power generation monitoring dataset that includes instantaneous power generation and cumulative power generation.

[0085] Among them, real-time power generation data Calculated using the following formula:

[0086] ;

[0087] in, for Real-time output power of the solar panels. for The output voltage of the solar panel at any given time. for The output current of the solar panel at any given time;

[0088] S13. Upload the environmental monitoring dataset and the power generation monitoring dataset to the cloud management platform for classified storage, and establish a data index table with time-series labels.

[0089] S2 performs solar power generation potential assessment based on environmental monitoring datasets and power generation monitoring datasets, and combines historical power generation databases to predict available power generation for future periods, generating power generation prediction data including the following steps:

[0090] S21. Extract cloud coverage and solar radiation intensity data from the environmental monitoring dataset, and combine them with the weather forecast API to obtain meteorological forecast data for the next 24 hours.

[0091] S22. Call the power generation statistical model of the same season and weather pattern in the historical power generation database, and use the time series regression algorithm based on long short-term memory network to calculate the expected power generation curve for the future period. The input feature vector based on the long short-term memory network model includes the power generation sequence of the historical 48 hours, the predicted solar irradiance and cloud coverage data for the next 24 hours, and the output is the predicted power generation for each hour of the next 24 hours.

[0092] S23. Based on the current state of charge of the battery and the expected charging efficiency, calculate the net available power generation for nighttime lighting and generate power generation prediction data with confidence intervals.

[0093] Among them, net available power generation Estimate using the following formula:

[0094] ;

[0095] In practice The table shows the overall charge-discharge efficiency as a function of battery temperature and aging, established through laboratory cycle life testing. The total energy consumption of auxiliary equipment is obtained by summing the measured standby power consumption of the controller, the polling power consumption of the sensor, and the intermittent working power consumption of the communication module.

[0096] in, This refers to the net available power generation during nighttime lighting hours. The overall charging and discharging efficiency of the battery. for The predicted power generation curve at any given time. and These are the start and end times for nighttime lighting. This refers to the total energy consumption of controllers, sensors, and other auxiliary equipment during the lighting period.

[0097] S3 classifies lighting demand levels based on power generation forecast data and environmental monitoring datasets, and determines road lighting priorities by combining preset pedestrian density data. The process of generating lighting demand classification data includes the following steps:

[0098] S31. Obtain road traffic flow statistics by time period, divide lighting time into peak time, off-peak time and low-peak time, and set the benchmark illuminance threshold for each time period;

[0099] Among them, the reference illuminance threshold for each time period Determined by the following empirical formula:

[0100] ;

[0101] in, For the first Reference illuminance thresholds for peak, off-peak, and low-peak periods. This is the lower limit of illuminance specified by national standards. To design the maximum permissible illuminance, For the first The weighting coefficient for each time period ranges from 0 to 1.

[0102] S32. Based on the net available power generation value in the power generation forecast data, classify the power supply level into three types: surplus power supply mode, balanced power supply mode and energy-saving operation mode.

[0103] S33. Construct a two-dimensional priority mapping matrix between power supply level and lighting time period, and set matching rules according to road safety management regulations: In the power saving operation mode, prioritize the protection of the benchmark illuminance of the main road during peak hours, and implement illuminance downgrading and color temperature shift to warm light strategy for secondary roads and off-peak hours, and generate lighting demand classification data including priority protection road sections and adjustable road sections.

[0104] In S4, multi-color temperature combination scheme matching is performed based on power generation forecast data and lighting demand classification data. The matching is performed against a preset color temperature and energy consumption relationship database to generate an appropriate color temperature configuration scheme. The generation of target color temperature configuration data includes the following steps:

[0105] S41. Pre-store the power consumption characteristic curves of LED combinations with different color temperatures, and establish a three-primary-color mixing energy consumption mapping table for 3000K warm white light, 4000K neutral light, and 5000K cool white light.

[0106] S42. Based on the power supply level in the lighting demand classification data, search the color temperature combination scheme that meets the illuminance standard and has energy consumption lower than the available power generation in the color temperature and energy consumption relationship database.

[0107] Among them, the total energy consumption of color temperature combination Evaluate using the following formula:

[0108] ;

[0109] In practice This refers to the steady-state power of LEDs in each color temperature channel under rated current, measured using an integrating sphere spectrometer in a constant temperature environment of 25℃. The actual duty cycle after nonlinear correction is controlled to an accuracy of less than 1%.

[0110] in, The total energy consumption of the selected color temperature combination within a set time period. The total number of color temperature channels participating in light mixing. For the first The power of each color temperature channel LED at its rated brightness. For the first The planned activation duration for each color temperature channel. For the first Dimming duty cycle of each color temperature channel;

[0111] S43. Based on the preset circadian rhythm and color temperature mapping table, after 22:00, prioritize the selection of warm color temperature configurations below 4000K. Calculate the comprehensive score of each candidate scheme through a weighted evaluation model. The comprehensive score is weighted and summed according to energy consumption weight and comfort weight. Select the scheme with the highest score and generate target color temperature configuration data containing the brightness ratio of each color temperature channel.

[0112] S5 drives the LED light source module to perform multi-color temperature mixing output adjustment based on the target color temperature configuration data. It controls the current duty cycle of LED chips with different color temperatures using pulse width modulation technology to generate actual color temperature output data, including the following steps:

[0113] S51. Analyze the target brightness values ​​of each color temperature channel in the target color temperature configuration data and convert them into the pulse width modulation duty cycle control signal of the corresponding LED driver;

[0114] Among them, the target brightness value is converted to Duty cycle The calculation formula is:

[0115] ;

[0116] In practice The method involves placing thermocouples on the LED heat sink substrate and measuring different junction temperatures. The luminous flux attenuation curve under the given conditions is used to fit the maximum brightness under the current operating conditions, and the compensation coefficient is calculated. It is a temperature derating coefficient calibrated based on the Arrhenius accelerated aging model to offset the light decay caused by high temperature;

[0117] in, For the first The final color temperature channel Duty cycle control quantity This is the target brightness value for this channel. This represents the maximum achievable brightness of the channel under the current conditions. Based on the current LED junction temperature The optical attenuation compensation coefficient;

[0118] S52. By adjusting the conduction time ratio of the three groups of LED chips (warm white light, neutral light, and cool white light) through a constant current drive circuit, a smooth transition within the continuous color temperature range of 2700K to 5700K is achieved.

[0119] S53. Real-time acquisition of LED junction temperature and luminous flux output data. Using an incremental proportional and integral closed-loop feedback control algorithm, the color temperature measured by the spectrometer is used as the feedback value to adjust the PWM duty cycle output, suppress color temperature deviation caused by temperature drift, and generate color temperature verification data corresponding to the actual output spectrum.

[0120] Based on actual color temperature output data, S6 monitors the dynamic distribution of vehicles and pedestrians within the illuminated area in real time. Combined with changes in ambient light intensity, it triggers dynamic fine-tuning of the color temperature, generating dynamic adjustment feedback data including the following steps:

[0121] S61. Use radar detectors and infrared thermal imagers to monitor the number, speed and distribution density of moving objects within the illuminated area;

[0122] S62. When a densely populated pedestrian area is detected, the medium and high color temperature components of the projector lights in that area are automatically increased to improve color rendering.

[0123] Among them, the color temperature shift for densely populated pedestrian areas Dynamically adjust using the following formula:

[0124] ;

[0125] In practice, pedestrian density It is the number of people per unit area counted after removing stationary obstacles, using a millimeter-wave radar point cloud clustering algorithm combined with infrared thermal imaging body temperature contour recognition, and a sensitivity coefficient. Based on road classification;

[0126] in, relative base color temperature The increase Pedestrian density per unit area The maximum permissible color temperature set for the system. The adjustment sensitivity coefficient is related to the road grade;

[0127] S63. When the ambient moonlight intensity exceeds the set threshold, the overall output power of the lamps is reduced and the light is shifted to a low color temperature warm light mode, generating dynamic adjustment feedback data that includes regionally differentiated adjustment parameters.

[0128] S7 integrates power generation prediction data, actual color temperature output data, and dynamic adjustment feedback data to construct an efficiency evaluation dataset. Adaptive strategy optimization is then performed using both energy efficiency and lighting quality as indicators to generate optimized system configuration data, including the following steps:

[0129] S71. Calculate the actual power generation per unit time and the total power consumption of the LED system, and calculate the energy utilization rate and energy storage buffer coefficient.

[0130] Among them, energy utilization rate index Calculated using the following formula:

[0131] ;

[0132] In practice The actual power consumption is obtained by bidirectional metering of the LED driver power input terminal using a high-precision power metering chip. This is the actual amount of solar power generated by the battery after deducting line transmission losses and inverter conversion losses.

[0133] in, To evaluate the percentage of energy utilization within the evaluation period, This refers to the actual electrical energy consumed by the LED lighting system. This refers to the total available electrical energy generated by the solar energy system during the same period.

[0134] S72. Collect average illuminance and uniformity data of the road surface through illuminance sensors, and quantify the lighting quality score by combining the glare index evaluation model.

[0135] S73. Establish a strategy optimization knowledge base based on historical adjustment records, and use a Q-learning-based reinforcement learning framework for strategy self-learning: Define the reward function as a linear weighted sum of energy utilization rate and lighting quality score. When energy shortages and lighting complaints occur continuously, automatically adjust the color temperature allocation weight and dimming strategy parameters in reverse to generate system optimization configuration data containing long-term optimization schemes.

[0136] It also includes maintenance and early warning procedures:

[0137] The system collects real-time data on the output efficiency degradation rate and surface contamination of solar panels. When the power generation efficiency drops below a preset threshold, a cleaning reminder signal is generated.

[0138] The system uses rainfall probability data from the environmental monitoring dataset to trigger a remote alarm if there is no rainfall in the short term. At the same time, during cleaning and maintenance, it controls the LED light source module to switch to a low-power, single low color temperature output mode.

[0139] The operation steps of a solar LED street light multi-color temperature adaptive adjustment system and method based on power generation are as follows:

[0140] I. Basic Data Collection Steps for Environmental and Power Generation:

[0141] After the system starts up, it first performs a data acquisition step. The meteorological sensor unit deployed on the top of the light pole collects real-time meteorological data such as temperature, humidity, and wind speed. At the same time, the light sensor obtains vertical and horizontal illuminance to build an environmental monitoring dataset with timestamps. Simultaneously, the power generation detection unit samples the output voltage and current in real time through a voltage transformer and a current sensor connected in series in the output circuit of the solar panel. The instantaneous power generation is obtained by processing the data according to the power calculation formula, and the working status parameters of the charge and discharge controller are recorded to form a power generation monitoring dataset containing instantaneous and cumulative power generation. All data is uploaded to the cloud management platform after verification, and a time-series tag index is established for subsequent analysis and retrieval.

[0142] II. Steps for assessing and predicting the potential of solar power generation:

[0143] Based on historical power generation databases and real-time environmental monitoring datasets, the system enters the power generation potential assessment stage. First, it extracts key environmental factors such as cloud cover and solar radiation intensity, and obtains future meteorological trends through a weather forecast interface. Then, it calls historical power generation statistical models under the same season and weather patterns, and uses time series analysis to fit the expected power generation curve for future periods. Furthermore, it combines the current state of charge and charge / discharge efficiency characteristics of the batteries, and calculates the net available power generation for achieving nighttime lighting by deducting the energy consumption of auxiliary equipment through an energy integral model. Finally, it generates power generation prediction data with confidence intervals, providing an energy constraint basis for lighting decisions.

[0144] III. Steps for classifying and processing road lighting demand:

[0145] Based on power generation forecast data and road attribute parameters, the system performs lighting demand classification. First, it obtains preset time-segmented pedestrian traffic density statistics and divides the lighting service period into three categories: peak, off-peak, and low-peak. Then, it dynamically calculates the benchmark illuminance threshold for each time period based on national standards and design limits. Subsequently, it classifies the power supply level into three modes: surplus, balanced, and energy-saving, based on the net available power generation value. By constructing a matching matrix between power supply level and time period, it determines the lighting priority of different road sections, marks the main road sections that need priority protection and the secondary road sections whose brightness can be reduced, and generates structured lighting demand classification data.

[0146] IV. Decision-making steps for matching multi-color temperature combination schemes:

[0147] In the multi-color temperature decision module, the system pre-stores power consumption characteristic curves of LED combinations with different color temperatures and establishes a three-primary-color mixing energy consumption mapping library from warm white light to cool white light. Based on the power supply level and illuminance requirements in the lighting demand classification data, the system searches the color temperature and energy consumption relationship library, selects feasible color temperature combination schemes that meet the illuminance standards and whose total energy consumption is lower than the available power generation, calculates the comprehensive score of each scheme through a weighted evaluation model, prioritizes the selection of comfortable color temperature configurations that conform to human circadian rhythm research, and finally outputs target color temperature configuration data containing the brightness ratio of each color temperature channel to achieve the optimization of visual experience under energy constraints.

[0148] V. LED Light Source Multi-Color Temperature Driving and Output Adjustment Steps:

[0149] The light source drive control module analyzes the target color temperature configuration data, converts the target brightness value of each channel into the corresponding pulse width modulation duty cycle control signal, and accurately adjusts the conduction time ratio of the three groups of LED chips (warm white, neutral white, and cool white) through a multi-channel constant current drive circuit to achieve a smooth spectral transition within a continuous color temperature range of 2700K to 5700K. Simultaneously, it collects LED junction temperature and luminous flux output feedback data in real time, and uses a closed-loop control algorithm to compensate for light decay and nonlinear distortion caused by temperature, maintain the stability of the output color temperature, and generate color temperature verification data that matches the actual spectral distribution.

[0150] VI. Dynamic Scene Perception and Color Temperature Fine-tuning Steps:

[0151] The dynamic sensing and adjustment module uses radar and infrared thermal imaging equipment to continuously monitor the distribution of moving objects within the illumination area, and to count vehicle speed and pedestrian density. When a densely populated pedestrian area is detected, the module automatically increases the mid-to-high color temperature components of the projected light in that area to enhance color rendering based on the density response function. When the ambient natural light intensity exceeds a set threshold, the module reduces the overall output power of the lamps and switches to a low color temperature warm light mode to reduce glare interference. All adjustment parameters are fed back in real time to form a closed loop, generating dynamic adjustment feedback data that includes regional differentiation strategies.

[0152] VII. System Performance Evaluation and Adaptive Strategy Optimization Steps:

[0153] The performance evaluation module integrates multi-source data such as power generation prediction, actual color temperature output, and dynamic adjustment feedback to construct a comprehensive evaluation dataset. It calculates energy utilization rate by measuring actual power generation and system power consumption, and evaluates lighting quality score by combining road surface illuminance uniformity and glare index. Based on historical operation records, it builds a strategy knowledge base. When energy shortages and user complaints occur continuously, the self-learning algorithm automatically adjusts the color temperature allocation weight and dimming strategy parameters to generate a system configuration scheme for long-term operation optimization, continuously improving energy utilization efficiency and lighting quality.

[0154] VIII. Photovoltaic Module Maintenance Early Warning and Linkage Control Steps:

[0155] As an additional step to ensure system reliability, the photovoltaic module tracks the output efficiency degradation rate and surface contamination indicators of the solar panel in real time. When the power generation efficiency decline exceeds a preset threshold, a module cleaning prompt signal is generated. If environmental monitoring data shows no natural rainfall in the short term, a remote maintenance alarm is triggered. During cleaning and maintenance, the system automatically controls the LED light source module to switch to a low-power monochromatic temperature output mode to reduce energy consumption, ensure battery safety, and guarantee continuous and stable basic lighting functions.

[0156] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A solar LED street light multi-color temperature adaptive adjustment system based on power generation, characterized in that the system... include: The data acquisition and monitoring module includes a meteorological sensor unit, a light sensor unit, and a power generation detection unit, which collect environmental parameters and power generation system operation data; The power generation potential prediction module, connected to the data acquisition and monitoring module, includes a historical data analysis unit, a meteorological correlation unit, and a power generation prediction unit, which generates predicted data on available power generation for future periods. The lighting demand analysis module receives the output data from the power generation potential prediction module, including a pedestrian flow statistics unit, a road classification unit, and a demand priority determination unit, to determine the lighting level configuration for different road sections. The multi-color temperature decision module, coupled with the power generation potential prediction module and the lighting demand analysis module, includes a color temperature and energy consumption mapping unit, a light mixing scheme generation unit and a comfort optimization unit, to generate a target color temperature configuration that is adapted to the current energy conditions. The light source drive control module, connected to the multi-color temperature decision module, includes a multi-channel PWM control unit, a constant current drive unit, and a spectral feedback unit, and performs accurate output adjustment of the multi-color temperature LED; The dynamic sensing and adjustment module forms a closed loop with the light source driving control module, including a motion detection unit, an ambient light sensing unit, and a local dimming unit; The performance evaluation and optimization module integrates the operational data from various modules, including the energy efficiency metering unit, the lighting quality assessment unit, and the strategy self-learning unit, to continuously optimize the system adjustment strategy.

2. A method for adaptive adjustment of multi-color temperature in solar LED streetlights based on power generation, characterized in that, The methods include: S1. Collect real-time meteorological data and light intensity data of the environment where the solar LED street light is located, and simultaneously collect the output voltage and current data of the solar panel to generate environmental monitoring dataset and power generation monitoring dataset. S2. Based on the environmental monitoring dataset and the power generation monitoring dataset, perform solar power generation potential assessment and processing, combine the historical power generation database to predict the available power generation in future periods, and generate power generation prediction data. S3. Based on the power generation prediction data and environmental monitoring dataset, perform lighting demand level classification processing, combine preset pedestrian flow density data to determine road lighting priority, and generate lighting demand level data. S4. Based on the power generation prediction data and lighting demand classification data, perform multi-color temperature combination scheme matching processing, match the preset color temperature and energy consumption relationship library to generate an appropriate color temperature configuration scheme, and generate target color temperature configuration data. S5. Drive the LED light source module to perform multi-color temperature mixed output adjustment according to the target color temperature configuration data, and control the current duty cycle of LED chips with different color temperatures through pulse width modulation technology to generate actual color temperature output data. S6. Based on actual color temperature output data, monitor the dynamic distribution of vehicles and pedestrians in the lighting area in real time, and trigger dynamic fine-tuning of color temperature in combination with changes in ambient light intensity to generate dynamic adjustment feedback data. S7. Integrate the power generation prediction data, actual color temperature output data and dynamic adjustment feedback data to construct an efficiency evaluation dataset. Optimize the system using adaptive strategies based on the dual indicators of energy efficiency and lighting quality to generate optimized system configuration data.

3. The method for adaptive adjustment of multi-color temperature of solar LED streetlights based on power generation according to claim 2, characterized in that, The steps in S1 to collect real-time meteorological data and light intensity data of the environment where the solar LED street light is located, and to simultaneously collect the output voltage and current data of the solar panel, and generate environmental monitoring datasets and power generation monitoring datasets, include the following: S11. Collect temperature, humidity, and wind speed data through meteorological sensors deployed on the top of the light pole, and collect vertical and horizontal illuminance data through light sensors to construct an environmental monitoring dataset containing timestamps. S12. Collect real-time power generation data by connecting the voltage transformer and current sensor in series in the output circuit of the solar panel, record the working status parameters of the charge and discharge controller, and construct a power generation monitoring dataset that includes instantaneous power generation and cumulative power generation. Among them, real-time power generation data Calculated using the following formula: ； in, for Real-time output power of the solar panels. for The output voltage of the solar panel at any given time. for The output current of the solar panel at any given time; S13. Upload the environmental monitoring dataset and the power generation monitoring dataset to the cloud management platform for classified storage, and establish a data index table with time-series labels.

4. The method for adaptive adjustment of multi-color temperature of solar LED streetlights based on power generation according to claim 2, characterized in that, The steps in S2 to perform solar power generation potential assessment based on the environmental monitoring dataset and power generation monitoring dataset, and to predict available power generation in future periods by combining historical power generation databases, to generate power generation prediction data include the following: S21. Extract cloud coverage and solar radiation intensity data from the environmental monitoring dataset, and combine them with the weather forecast API to obtain meteorological forecast data for the next 24 hours. S22. Call the power generation statistical model of the same season and weather pattern in the historical power generation database, and use the time series analysis method to calculate the expected power generation curve for future periods. S23. Based on the current state of charge of the battery and the expected charging efficiency, calculate the net available power generation for nighttime lighting and generate power generation prediction data with confidence intervals.

5. The method for adaptive adjustment of multi-color temperature of solar LED streetlights based on power generation according to claim 2, characterized in that, In step S3, the lighting demand level classification is performed based on the power generation prediction data and the environmental monitoring dataset. Combined with preset pedestrian density data, the priority of road lighting is determined, and the generation of lighting demand level data includes the following steps: S31. Obtain road traffic flow statistics by time period, divide lighting time into peak time, off-peak time and low-peak time, and set the benchmark illuminance threshold for each time period; S32. Based on the net available power generation value in the power generation prediction data, classify the power supply into three levels: surplus power supply mode, balanced power supply mode and energy-saving operation mode. S33. Perform matrix matching between power supply level and lighting time period category to generate lighting demand classification data including priority protection sections and adjustable section lighting demand classification data.

6. The method for adaptive adjustment of multi-color temperature in solar LED streetlights based on power generation according to claim 2, characterized in that, In step S4, based on the predicted power generation data and the lighting demand classification data, a multi-color temperature combination scheme matching process is performed. This process matches the preset color temperature and energy consumption relationship database to generate a suitable color temperature configuration scheme. Generating the target color temperature configuration data includes the following steps: S41. Pre-store the power consumption characteristic curves of LED combinations with different color temperatures, and establish a three-primary-color mixing energy consumption mapping table for 3000K warm white light, 4000K neutral light, and 5000K cool white light. S42. Based on the power supply level in the lighting demand classification data, retrieve color temperature combination schemes that meet the illuminance standard and have energy consumption lower than the available power generation in the color temperature and energy consumption relationship database. Among them, the total energy consumption of color temperature combination Evaluate using the following formula: ； in, The total energy consumption of the selected color temperature combination within a set time period. The total number of color temperature channels participating in light mixing. For the first The power of each color temperature channel LED at its rated brightness. For the first The planned activation duration for each color temperature channel. For the first Dimming duty cycle of each color temperature channel; S43. Based on human comfort research data, prioritize the color temperature configuration that conforms to the circadian rhythm and generate target color temperature configuration data that includes the brightness ratio of each color temperature channel.

7. The method for adaptive adjustment of multi-color temperature of solar LED streetlights based on power generation according to claim 2, characterized in that, In step S5, the LED light source module is driven to perform multi-color temperature mixing output adjustment according to the target color temperature configuration data. The current duty cycle of LED chips with different color temperatures is controlled by pulse width modulation technology to generate actual color temperature output data, including the following steps: S51. Analyze the target brightness values ​​of each color temperature channel in the target color temperature configuration data and convert them into the pulse width modulation duty cycle control signal of the corresponding LED driver; S52. Adjust the on-time ratio of the three groups of LED chips (warm white, neutral white, and cool white) through a constant current drive circuit. S53. Real-time acquisition of LED junction temperature and luminous flux output data, maintaining color temperature stability through closed-loop feedback control, and generating color temperature verification data corresponding to the actual output spectrum.

8. The method for adaptive adjustment of multi-color temperature of solar LED streetlights based on power generation according to claim 2, characterized in that, The steps in S6, which involve real-time monitoring of the dynamic distribution of vehicles and pedestrians within the illumination area based on actual color temperature output data, and triggering dynamic fine-tuning of color temperature in conjunction with changes in ambient light intensity to generate dynamic adjustment feedback data, include the following: S61. Use radar detectors and infrared thermal imagers to monitor the number, speed and distribution density of moving objects within the illuminated area; S62. When a densely populated pedestrian area is detected, automatically increase the medium and high color temperature components of the projection lights in that area; S63. When the ambient moonlight intensity exceeds the set threshold, the overall output power of the lamps is reduced and the light is shifted to a low color temperature warm light mode, generating dynamic adjustment feedback data that includes regionally differentiated adjustment parameters.

9. The method for adaptive adjustment of multi-color temperature in solar LED streetlights based on power generation according to claim 2, characterized in that, The S7 step involves integrating the predicted power generation data, actual color temperature output data, and dynamic adjustment feedback data to construct an efficiency evaluation dataset. Adaptive strategy optimization is then performed using both energy efficiency and lighting quality indicators to generate optimized system configuration data, including the following steps: S71. Calculate the actual power generation per unit time and the total power consumption of the LED system, and calculate the energy utilization rate and energy storage buffer coefficient. S72. Collect average illuminance and uniformity data of the road surface through illuminance sensors, and quantify the lighting quality score by combining the glare index evaluation model. S73. Establish a strategy optimization knowledge base based on historical adjustment records. When energy shortages and lighting complaints occur continuously, automatically adjust the color temperature allocation weight and dimming strategy to generate system optimization configuration data containing long-term optimization solutions.

10. The method for adaptive adjustment of multi-color temperature of solar LED streetlights based on power generation according to claim 2, characterized in that, It also includes maintenance and early warning procedures: The system collects real-time data on the output efficiency degradation rate and surface contamination of solar panels. When the power generation efficiency drops below a preset threshold, a cleaning reminder signal is generated. The system uses rainfall probability data from the environmental monitoring dataset to trigger a remote alarm if there is no rainfall in the short term. Simultaneously, during cleaning and maintenance, it controls the LED light source module to switch to a low-power, single low color temperature output mode.

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