LED Outdoor Light Environmental Adaptability Spectrum Adjustment System and Method

Abstract

The present invention belongs to the field of lighting technology, and particularly relates to an environmental adaptability spectrum adjustment system and method for LED outdoor lights. The system includes three major modules: environmental perception, intelligent control, and spectrum regulation. The environmental perception module monitors in real time the distance, quantity, direct radiation direction, and lighting interval between the lighting points and the target points in the lighting area, constructs a photosensitive tracking matrix based on this, and obtains a spectrum adjustment factor by combining environmental parameters and a preset environmental factor function. The intelligent control module intelligently regulates the turning-on sequence and switching time of the lighting points to the target points according to the photosensitive tracking matrix and the differential control model. The spectrum regulation module uses the spectrum adjustment factor to adjust the spectrum of the lighting points to the standard lighting spectrum preset based on the CIE standard chromaticity space and the ecological sensitive spectrum, so as to obtain a synthesized spectrum that meets the requirements of the target points, effectively achieving energy conservation and consumption reduction and adapting to different environmental lighting requirements.

Description

Technical Field

[0001] The present invention belongs to the field of lighting adjustment, and particularly relates to an environmental adaptability spectrum adjustment system and method for LED outdoor lights. Background Art

[0002] LED outdoor lights have been widely used in bridge lighting and vehicle guidance due to their advantages such as energy saving and long lifespan. However, there are many deficiencies in the existing bridge vehicle LED guidance light systems. Currently, most LED guidance lights on bridges lack an adaptive mechanism in the opening mode and often adopt a fixed opening mode, unable to intelligently adjust according to environmental factors such as actual traffic flow and weather conditions. As a result, when there are fewer vehicles, a large number of LED lights are still fully turned on, causing energy waste and increasing the degree of light pollution. Moreover, the farther the distance under direct light, the greater the impact on people's vision, and the existing systems cannot precisely control the intensity, direction, and spectrum of light, bringing potential safety hazards to driving safety during driving.

[0003] For example, the patent with the authorization announcement number CN109451624B discloses a spectrum adjustment method for a multi-channel LED lighting system, including the following steps: presetting a spectrum adjustment target variable and its requirements according to lighting application needs; generating a synthetic spectrum of the multi-channel LED lighting system; calculating the target variable value of the synthetic spectrum; determining whether the target variable value of the synthetic spectrum meets the requirements of the preset spectrum adjustment target variable. If not, regenerating the synthetic spectrum of the multi-channel LED lighting system until the target variable value of the obtained synthetic spectrum meets the requirements of the preset spectrum adjustment target variable, and obtaining a synthetic spectrum that meets lighting application needs.

[0004] For example, the patent application with the publication number CN117156626A discloses a spectrum adjustment method and system based on LED lights. The method includes: respectively obtaining the actual values of the five-color spectrum; the five-color spectrum includes red light, blue light, white light, far red light, and ultraviolet light; determining whether the actual values of the five-color spectrum are valid; if the actual values of the five-color spectrum are valid, respectively calculating the DIM values of the five-color spectrum; generating a dimming range according to the multiple DIM values of the five-color spectrum; if the actual values of the five-color spectrum are invalid, turning off the power supply. Summary of the Invention

[0005] In view of the deficiencies of the prior art, the present invention proposes an LED outdoor lamp environmental adaptability spectrum adjustment system and method, which includes three major modules: environmental perception, intelligent control, and spectrum regulation; the environmental perception module monitors the distance, quantity, directivity, and lighting interval between the lighting points and the target points in the lighting area in real time, constructs a photosensitive tracking matrix based on this, and obtains a spectrum adjustment factor by combining environmental parameters and a preset environmental factor function. The intelligent control module intelligently regulates the opening sequence and switching time of the lighting points to the target points according to the photosensitive tracking matrix and the differential control model; the spectrum regulation module uses the spectrum adjustment factor to adjust the spectrum of the lighting points to the standard lighting spectrum preset based on the CIE standard chromaticity space and the ecological sensitive spectrum, so as to obtain a synthetic spectrum that meets the requirements of the target points, effectively achieving energy conservation and consumption reduction and adapting to different environmental lighting requirements.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An LED outdoor lamp environmental adaptability spectrum adjustment method, including:

[0008] According to the distance between each lighting point and the target point, the number of target points, the directivity of the lighting point corresponding to the target point, and the lighting interval obtained in real time within the preset LED lighting array area, construct a photosensitive tracking matrix;

[0009] At the same time, according to the real-time environmental parameters of the LED lighting array area and the preset environmental factor function, obtain a spectrum adjustment factor;

[0010] According to the photosensitive tracking matrix and the configured differential control model, generate a differential control instruction to control the opening sequence and switching time points of each lighting point corresponding to the target point;

[0011] When the lighting point is turned on, based on the spectrum adjustment factor, adjust the spectrum corresponding to each turned-on lighting point until the preset standard lighting spectrum obtained based on the CIE standard chromaticity space and the ecological sensitive spectrum in the environment is satisfied, and obtain a synthetic spectrum that meets the requirements of the target points.

[0012] Specifically, the steps of constructing the photosensitive tracking matrix include:

[0013] Extract the range interval of the target area, the distribution state of the lighting points in the target area, the number of lighting points, and the lighting parameters of a single lighting point from the LED lighting array;

[0014] Based on the light power, main beam angle, floodlight scattering angle, maximum effective irradiation distance, and light attenuation coefficient corresponding to the lighting parameters of each lighting point, perform lighting point spot modeling through the double Gaussian light distribution model combined with the environmental factor function to obtain the dynamic lighting spot model corresponding to each lighting point and the original dynamic interval of the corresponding lighting spot in the traveling direction;

[0015] The light attenuation coefficient is obtained through a simulation experiment based on the characteristic relationship among light power, light intensity, and distance.

[0016] Based on the dynamic illumination spot models corresponding to two adjacent illumination points in the same traveling direction and the maximum illumination interval ranges corresponding to the two illumination points, the overlapping redundant interval corresponding to two adjacent illumination points in the same traveling direction is obtained through integral of the light distribution curve and the Monte Carlo approximation algorithm.

[0017] Repeat the above process to obtain the overlapping redundant intervals corresponding to all adjacent illumination points, and obtain an overlapping redundant interval set.

[0018] Specifically, the steps of constructing the photosensitive tracking matrix further include:

[0019] Subtract the product of the interval length of the overlapping redundant interval corresponding to the first and second illumination points in the same traveling direction and the dynamic attenuation factor from the interval of the illumination spot corresponding to the second illumination point in the traveling direction to obtain the effective dynamic illumination interval of the second illumination point in the traveling direction.

[0020] Input the effective dynamic illumination interval of the second illumination point in the traveling direction into the edge light intensity enhancement algorithm to correct the light intensity energy of the subtracted partial interval, and obtain the corrected effective dynamic illumination interval of the second illumination point in the traveling direction.

[0021] The dynamic attenuation factor is constructed based on the real-time moving speed of the target point and the reference moving speed.

[0022] Repeat the process of obtaining the effective dynamic illumination interval of the second illumination point in the traveling direction to obtain a set of corrected effective dynamic illumination intervals in the same traveling direction.

[0023] Specifically, the steps of constructing the photosensitive tracking matrix further include:

[0024] Obtain the real-time position, instantaneous velocity sequence, distance to the nearest illumination point in the front of the traveling direction, and system delay corresponding to each illumination point of the target point in the set of effective dynamic illumination intervals, and construct a target point movement trajectory prediction data set.

[0025] Input the target point movement trajectory prediction data set into the compensation movement trajectory function constructed by the support vector machine, and obtain the movement trajectory and real-time position points corresponding to the compensated target point according to the actual displacement of the target point and the displacement distance corresponding to the illumination point delay.

[0026] Based on the movement trajectory and real-time position points corresponding to the compensated target point and the set of corrected effective dynamic illumination intervals in the same traveling direction, obtain the set of control time points corresponding to each target point reaching the lower limit of each corrected effective dynamic illumination interval.

[0027] Specifically, the steps of constructing the photosensitive tracking matrix further include:

[0028] Based on the set of control time points, a photosensitive tracking matrix is constructed through a 0-1 matrix , specifically:

[0029] where represents the illumination-tracking factor of the i-th target point in the corrected effective dynamic illumination interval corresponding to the j-th illumination point, represents the real-time running time length of the i-th target point in the corrected effective dynamic illumination interval corresponding to the j-th illumination point, represents the time length when the i-th target point runs to the lower limit of the interval in the corrected effective dynamic illumination interval corresponding to the j-th illumination point, represents the switch delay time length corresponding to the j-th illumination point.

[0030] Specifically, the process of obtaining the spectral adjustment factor includes:

[0031] The illumination time period is divided into periods of two hours each. Based on the ecological sensitive spectrum, the photosensitive band ranges of all relevant organisms in the illuminated area and the corresponding physiological function influence data are collected and sorted out;

[0032] The collected photosensitive band ranges are classified and stored to establish a biological-time-photosensitive band database;

[0033] According to the spectrum of the corresponding illumination point and the biological-time-photosensitive band database, the overlap degree of each biological photosensitive band is obtained;

[0034] Based on the overlap degree of each biological photosensitive band, the ecological sensitive spectrum constraint factor corresponding to each time period is obtained;

[0035] Using the obtained ecological sensitive spectrum constraint factor, according to the target demand spectrum, the spectrum corresponding to each illumination point is filtered to obtain the filtered spectrum.

[0036] Specifically, obtaining the spectral adjustment factor further includes:

[0037] Based on the CIE standard chromaticity space in the environment and the real chromaticity coordinates corresponding to the filtered spectrum of the real-time illumination point, the required chromaticity deviation is obtained;

[0038] Based on the required chromaticity deviation, the chromaticity deviation is corrected through a Gaussian attenuation function to obtain the spectrum with corrected chromaticity;

[0039] Based on the spectrum completed by chromaticity correction, a visual adaptability adjustment factor is constructed using the temperature and humidity obtained through the color temperature, color rendering index, glare index, and meteorological simulation model, and the light intensity of the spectrum completed by chromaticity correction is adjusted to obtain the spectrum with the light intensity adjusted completed.

[0040] According to the obtained ecological sensitive spectrum constraint factor, visual adaptability adjustment factor, and chromaticity correction process, through the genetic algorithm combined with the constraint conditions constructed by the real-time light parameters and required light parameters of the lighting points, and the spectrum adjustment function fitted by the ecological sensitive spectrum constraint factor, visual adaptability adjustment factor, and chromaticity deviation, training is carried out to obtain the trained spectrum adjustment function and the corresponding weight parameters.

[0041] Specifically, the configuration process of the differential control model includes:

[0042] Based on reinforcement learning combined with the spectrum adjustment function, photosensitive tracking matrix, and preset adjustment trigger conditions, a differential control model is constructed.

[0043] Obtain data on different traffic flows, driving speeds, different weather conditions, and natural light conditions in the corresponding lighting area at different time periods to train the differential control model, and embed the trained differential control model into the corresponding lighting area for real-time LED lighting control.

[0044] Real-time collect the evaluation information of the current lighting control process through the target points passing through the target lighting area through an online questionnaire.

[0045] According to the evaluation information, through a comprehensive evaluation algorithm, obtain the corresponding regulation evaluation score.

[0046] Construct a reinforcement reward function according to the regulation evaluation score, and feedback the reinforcement reward function to the differential control model for continuous training to obtain the differential control model under continuous training.

[0047] Specifically, the preset adjustment trigger conditions include:

[0048] When the natural light intensity is greater than the preset lighting threshold, all lighting points are in the off state, otherwise, all lighting points are in the silent state.

[0049] When all lighting points are in the silent state, when at least one target point enters the starting position point of the effective dynamic lighting interval, turn on the corresponding lighting points through the photosensitive tracking matrix, and synchronously adjust the spectrum of the turned-on lighting points according to the real-time collected lighting point parameters, weather condition parameters, and natural light parameters through the spectrum adjustment function.

[0050] When the target point reaches the position point corresponding to the lower limit of the effective dynamic lighting range and there is no target point in the current effective dynamic lighting range, the corresponding lighting point is converted into a silent state through the photosensitive tracking matrix;

[0051] Otherwise, the current lighting point is in the on state, and the above process is repeated to adjust the spectrum of the current lighting point in real time according to the parameters of the target point and the lighting point within the corresponding effective dynamic lighting range;

[0052] When the number of target points corresponding to at least one effective dynamic lighting range exceeds the preset peak threshold, the number of lighting points turned on simultaneously is adjusted so that the corresponding light intensity can meet the lighting requirements of all target points at the same time.

[0053] The spectral adjustment system for the environmental adaptability of LED outdoor lights includes: an environmental perception module, an intelligent control module, and a spectral regulation module;

[0054] The environmental perception module is used to construct a photosensitive tracking matrix according to the distance between each lighting point and the target point, the number of target points, the direct direction of the target point corresponding to the lighting point, and the lighting range obtained in real time within the preset LED lighting array area. At the same time, according to the real-time environmental parameters of the LED lighting array area and the preset environmental factor function, a spectral adjustment factor is obtained;

[0055] The intelligent control module controls the turning-on sequence and the switching time point of the target point corresponding to each lighting point based on the photosensitive tracking matrix combined with the configured differential control model;

[0056] The spectral regulation module adjusts the spectrum of each turned-on lighting point based on the spectral adjustment factor until the preset standard lighting spectrum obtained based on the CIE standard chromaticity space and the ecological sensitive spectrum under the environment is satisfied, and a synthetic spectrum that meets the requirements of the target point is obtained.

[0057] Compared with the prior art, the beneficial effects of the present invention are:

[0058] In view of the deficiencies of the prior art, the present invention constructs a photosensitive tracking matrix by using an environmental perception module to monitor the distance, quantity, direct illumination direction, and illumination range between the illumination points and the target points in real time. It can intelligently judge the turning-on conditions of the illumination points according to the actual situation, avoiding fully turning on all the LED lights when there are few vehicles and other situations where full illumination is not required, effectively solving the problems of energy waste and light pollution. The spectral adjustment factor is obtained by using the environmental factor function, and combined with the differential control model of the intelligent control module, the turning-on sequence and switching time points of the illumination points are precisely controlled to meet the illumination requirements in different environments. At the same time, based on the spectral adjustment factor, the spectrum of the turned-on illumination points is adjusted to conform to the preset standard illumination spectrum based on the CIE standard chromaticity space and the ecologically sensitive spectrum, realizing precise control of the light intensity, direction, and spectrum, avoiding affecting driving safety due to light problems, and comprehensively improving the intelligence, energy-saving performance, and safety of the LED guiding light system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is a flowchart of the method for spectral adjustment of the environmental adaptability of the LED outdoor lamp in Embodiment 1 of the present invention;

[0060] Figure 2 It is an example diagram of the coverage redundancy interval in Embodiment 1 of the present invention;

[0061] Figure 3 It is a module diagram of the system for spectral adjustment of the environmental adaptability of the LED outdoor lamp in Embodiment 2 of the present invention.

[0062] Reference numerals: 1, coverage redundancy interval; 2, the first original dynamic interval, 3, the second original dynamic interval, 4, the illumination point corresponding to the first original dynamic interval. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0063] Embodiment 1

[0064] Please refer to Figure 1 , an embodiment provided by the present invention: A method for spectral adjustment of the environmental adaptability of an LED outdoor lamp, including:

[0065] S1. Monitor the distance between each illumination point and the target point, the quantity of the target points, the direct illumination direction of the illumination point corresponding to the target point, and the illumination range within the preset LED illumination array area in real time, and construct a photosensitive tracking matrix according to the distance between each illumination point and the target point, the quantity of the target points, the direct illumination direction of the illumination point corresponding to the target point, and the illumination range obtained in real time within the preset LED illumination array area;

[0066] S2. At the same time, obtain a spectral adjustment factor according to the real-time environmental parameters of the LED illumination array area in combination with the preset environmental factor function;

[0067] S3. Generate a differential control instruction according to the photosensitive tracking matrix in combination with the configured differential control model to control the turning-on sequence and switch time points of each lighting point corresponding to the target point;

[0068] S4. While the lighting point is turned on, based on the spectral adjustment factor, adjust the spectrum corresponding to each turned-on lighting point until the preset standard lighting spectrum obtained based on the CIE standard chromaticity space and the ecological sensitive spectrum in the environment is satisfied, and obtain the synthetic spectrum that meets the requirements of the target point.

[0069] Further, in this embodiment, in order to more clearly illustrate the meaning of controlling the turning-on sequence and switch time points of each lighting point corresponding to the target point, assume that the target point is a running vehicle. Here, the LED lights corresponding to each lighting point control the sequence and specific time points of turning on corresponding to each lighting point according to the lighting section corresponding to the vehicle operation, the specific position points, and the vehicle speed.

[0070] Further, the steps for constructing the photosensitive tracking matrix in this embodiment include:

[0071] Extract the range interval of the target area, the distribution state of the lighting points in the target area, the number of lighting points, and the lighting parameters of a single lighting point from the LED lighting array;

[0072] Based on the light power, main beam angle, floodlight scattering angle, maximum effective irradiation distance, and light attenuation coefficient corresponding to the lighting parameters of each lighting point, perform lighting point spot modeling through the double Gaussian light distribution model in combination with the environmental factor function to obtain the dynamic lighting spot model corresponding to each lighting point and the original dynamic interval of the corresponding lighting spot in the traveling direction;

[0073] The light attenuation coefficient is obtained through simulation experiments based on the characteristic relationship between light power, light intensity, and distance;

[0074] Based on the dynamic lighting spot models corresponding to two adjacent lighting points in the same traveling direction and the maximum lighting interval ranges corresponding to the two lighting points, obtain the coverage redundancy interval corresponding to two adjacent lighting points in the same traveling direction through the light distribution curve integration and Monte Carlo approximation algorithm;

[0075] Repeat the above process to obtain the coverage redundancy intervals corresponding to all adjacent lighting points to obtain a set of coverage redundancy intervals;

[0076] Subtract the product of the interval length of the coverage redundancy interval corresponding to the first and second lighting points in the same traveling direction and the dynamic attenuation factor from the interval of the lighting spot corresponding to the second lighting point in the traveling direction to obtain the effective dynamic lighting interval of the second lighting point in the traveling direction;

[0077] Further, please refer toFigure 2 , 1 represents the coverage redundancy interval, 2 represents the first original dynamic interval, 3 represents the second original dynamic interval, 4 represents the illumination point corresponding to the first original dynamic interval; the straight line corresponding to L1 represents the length of the effective dynamic illumination interval corresponding to the first original dynamic interval, and the dashed line corresponding to L2 represents the length of the effective dynamic illumination interval corresponding to the second original dynamic interval; the length of the effective dynamic illumination interval corresponding to the subsequent original dynamic intervals is the same as that of L2; is the maximum illumination angle corresponding to the illumination point 4 corresponding to the first original dynamic interval; here, the coverage redundancy interval corresponding to the second original dynamic interval 3 in the overlapping coverage redundancy interval 1 between the first original dynamic interval 2 and the second original dynamic interval 3 is removed.

[0078] Input the effective dynamic illumination interval of the second illumination point in the traveling direction into the edge light intensity enhancement algorithm to correct the subtracted partial interval light intensity energy, and obtain the corrected effective dynamic illumination interval of the second illumination point in the traveling direction;

[0079] Further, the dynamic attenuation factor in this embodiment is constructed through the real-time moving speed of the target point and the reference moving speed. The specific process includes:

[0080] First, set the reference moving speed as the standard reference value (such as system preset or historical average speed), and real-time monitor the current moving speed of the target point; calculate the absolute value of the difference between the real-time moving speed and the reference moving speed. If the real-time moving speed exceeds the reference moving speed, the dynamic attenuation factor is reduced according to the ratio of the excess speed to the reference moving speed (for example, for every 10% increase in the difference, the attenuation factor is reduced by 5%), otherwise it is increased; at the same time, the dynamic attenuation factor is adjusted in real time in combination with the cosine value of the angle between the moving direction of the target point and the direct illumination direction of the illumination point (the smaller the change value of the angle, the greater the adjustment weight of the attenuation factor).

[0081] Repeat the process of obtaining the effective dynamic illumination interval of the second illumination point in the traveling direction to obtain the set of corrected effective dynamic illumination intervals in the same traveling direction;

[0082] Obtain the real-time position, instantaneous speed sequence, distance from the target point to the nearest illumination point in the forward direction of travel, and the system delay corresponding to each illumination point in the set of effective dynamic illumination intervals, and construct a target point movement trajectory prediction data set;

[0083] Input the target point movement trajectory prediction data set into the compensation movement trajectory function constructed by the support vector machine, and obtain the compensated movement trajectory and real-time position point corresponding to the target point according to the actual displacement of the target point and the displacement distance corresponding to the illumination delay;

[0084] Based on the movement trajectory corresponding to the compensated target point, the real-time position point, and the corrected set of effective dynamic lighting intervals in the same traveling direction, obtain the set of control time points corresponding to each target point reaching the lower limit of each corrected effective dynamic lighting interval;

[0085] Based on the set of control time points, construct a photosensitive tracking matrix through a 0-1 matrix , specifically:

[0086] Among them, represents the illumination-tracking factor of the i-th target point in the corrected effective dynamic lighting interval corresponding to the j-th lighting point, represents the real-time running time length of the i-th target point in the corrected effective dynamic lighting interval corresponding to the j-th lighting point, represents the time length when the i-th target point runs to the lower limit of the corrected effective dynamic lighting interval corresponding to the j-th lighting point, represents the switch delay time length corresponding to the j-th lighting point.

[0087] This photosensitive tracking matrix dynamically models the spot distribution in the lighting area and the movement trajectory of the target point, combines the double Gaussian light distribution model and the Monte Carlo algorithm to calculate the coverage redundant interval between adjacent lighting points, and introduces a dynamic attenuation factor to correct the length of the effective dynamic lighting interval based on the real-time moving speed of the target point; by mapping the spatial distance into time parameters (real-time running time, interval lower limit time, and switch delay time), construct a 0-1 matrix control rule: turn on the lighting when the residence time of the target point does not exceed "lower limit time + delay time", otherwise turn it off. For example, in a highway scenario, the system accurately calculates a 1.2-second time window corresponding to a 30-meter effective dynamic lighting interval according to the vehicle speed (such as 25 m / s), combined with a 0.2-second hardware delay, to ensure that the lighting turns off just before the vehicle leaves, avoiding both the waste of energy due to spot overlap and the problem of lighting lag, and finally achieving an optimized effect with a dynamic response error less than 0.1 second and an energy consumption reduction of more than 30%.

[0088] This process first provides rich and basic data support for subsequent analysis by extracting the range intervals, illumination point distribution states, quantities, and illumination parameters of the LED lighting array area and the target area. Based on the illumination power, main beam angle, floodlight scattering angle, maximum effective illumination distance, and illumination attenuation coefficient, a double Gaussian light distribution model combined with an environmental factor function is used for spot modeling, which can accurately depict the dynamic illumination spot model and the original dynamic interval of each illumination point. This makes the simulation of the illumination effect by the lighting system more in line with the actual scenario. Secondly, by using the light distribution curve integral and the Monte Carlo approximation algorithm, the coverage redundancy interval between adjacent illumination points is calculated and the coverage redundancy interval set is obtained, enabling a clear understanding of the illumination overlapping area. On this basis, a dynamic attenuation factor is constructed in combination with the real-time moving speed of the target point to determine the effective dynamic illumination interval, and it is corrected through the edge light intensity enhancement algorithm, which not only avoids the waste of illumination resources but also ensures the uniformity and stability of illumination and improves the energy utilization efficiency of the lighting system. Finally, through the target point movement trajectory prediction data set, a compensated movement trajectory function is constructed, fully considering the actual displacement of the target point and the displacement distance corresponding to the illumination delay, so as to obtain the accurate compensated target point movement trajectory and real-time position points. Based on this, in combination with the corrected effective dynamic illumination interval set, a control time point set is determined and a photosensitive tracking matrix is constructed to realize the intelligent and accurate control of the switching timing of the illumination points by the lighting system according to the real-time state of the target point, ensuring that the lighting system can meet the illumination requirements while minimizing energy consumption and improving the intelligent level and overall performance of the lighting system.

[0089] Furthermore, the process of obtaining the spectral adjustment factor in this embodiment includes:

[0090] The illumination time period is divided into time periods of two hours each, and the photosensitive band ranges and corresponding physiological function influence data of all relevant organisms in the illumination area within each time period are collected and sorted based on the ecologically sensitive spectrum.

[0091] The collected photosensitive band ranges are classified and stored to establish a biological-time-photosensitive band database.

[0092] According to the spectrum of the corresponding illumination point and the biological-time-photosensitive band database, the overlap degree of each biological photosensitive band is obtained; based on the overlap degree of each biological photosensitive band, the ecologically sensitive spectrum constraint factor corresponding to each time period is obtained.

[0093] Using the obtained ecologically sensitive spectrum constraint factor, according to the target required spectrum, the spectrum corresponding to each illumination point is filtered to obtain the filtered spectrum.

[0094] Based on the CIE standard chromaticity space in the environment and the true chromaticity coordinates corresponding to the filtered spectrum of the real-time illumination point, the required chromaticity deviation is obtained.

[0095] Based on the required chromaticity deviation, the chromaticity deviation is corrected through a Gaussian attenuation function to obtain a spectrum with completed chromaticity correction.

[0096] Based on the spectrum with completed chromaticity correction, a visual adaptability adjustment factor is constructed through the color temperature, color rendering index, glare index, and temperature and humidity obtained from the meteorological simulation model, and the light intensity of the spectrum with completed chromaticity correction is adjusted to obtain a spectrum with completed light intensity adjustment.

[0097] According to the obtained ecological sensitivity spectrum constraint factor, visual adaptability adjustment factor, and chromaticity correction process, training is carried out through the genetic algorithm combined with the constraint conditions constructed by the real-time lighting parameters and required lighting parameters of the lighting points, as well as the spectrum adjustment function fitted by the ecological sensitivity spectrum constraint factor, visual adaptability adjustment factor, and chromaticity deviation, to obtain a trained spectrum adjustment function and corresponding weight parameters.

[0098] Furthermore, the configuration process of the differential control model in this embodiment includes:

[0099] Based on reinforcement learning combined with the spectrum adjustment function, photosensitive tracking matrix, and preset adjustment trigger conditions, a differential control model is constructed.

[0100] Furthermore, the preset adjustment trigger conditions in this embodiment include:

[0101] When the natural light intensity is greater than the preset lighting threshold, all lighting points are in the off state; otherwise, all lighting points are in the silent state.

[0102] When all lighting points are in the silent state, when at least one target point enters the starting position point of the effective dynamic lighting interval, the corresponding lighting point is turned on through the photosensitive tracking matrix, and the spectrum of the turned-on lighting point is synchronously adjusted through the spectrum adjustment function according to the real-time collected lighting point parameters, weather condition parameters, and natural light parameters.

[0103] When the target point reaches the position point corresponding to the lower limit of the effective dynamic lighting interval and there is no target point in the current effective dynamic lighting interval, the corresponding lighting point is converted into the silent state through the photosensitive tracking matrix.

[0104] Otherwise, the current lighting point is in the on state, and according to the target point and lighting point parameters in the corresponding effective dynamic lighting interval, the above process is repeated to perform real-time adjustment of the spectrum corresponding to the current lighting point.

[0105] When the number of target points corresponding to at least one effective dynamic lighting interval exceeds the preset peak threshold, the number of lighting points turned on simultaneously is adjusted so that the corresponding light intensity simultaneously meets the lighting requirements of all target points.

[0106] Obtain data on different traffic flows, driving speeds, different weather conditions, and natural light conditions in the corresponding lighting areas for different time periods to train the differential control model, and embed the trained differential control model into the corresponding lighting area for real-time LED lighting control;

[0107] Collect the evaluation information of the current lighting control process at the target point passing through the target lighting area in real time through an online questionnaire;

[0108] According to the evaluation information, obtain the corresponding regulation evaluation score through a comprehensive evaluation algorithm;

[0109] Construct a reinforcement reward function based on the regulation evaluation score, and feedback the reinforcement reward function to the differential control model for continuous training to obtain the differential control model under continuous training.

[0110] In this embodiment, with the help of dynamic spectrum adjustment and intelligent control models, an accurate balance among ecological protection, visual comfort, and energy consumption optimization is achieved in a certain coastal tunnel lighting system. During ecologically sensitive periods, such as the peak period of insect phototaxis from 22:00 to 24:00 at night, the system restricts the light intensity that overlaps highly with the light-sensitive bands of insects according to the biological-time-photosensitive band database, and reconstructs the spectrum through a genetic algorithm, effectively reducing the light stress response of insects or birds. In the face of bad weather, such as during heavy rain, the system uses a meteorological simulation model to perceive environmental parameters, triggers a visual adaptability adjustment factor, increases the color temperature and color rendering index, and enhances the driver's recognition distance of obstacles. During peak traffic flow, the differential control model dynamically adjusts the opening of lighting points through reinforcement learning. While meeting the road surface illumination requirements, the energy consumption increase is much lower than that of traditional systems. In addition, by constructing a regulation evaluation score through a large number of user feedbacks, a dynamic weight optimization system is introduced to reduce the glare index and complaint rate. In the process of constructing the spectrum adjustment factor and the differential control model, in terms of spectrum adjustment, by dividing the lighting time period, studying biological photosensitive data to obtain ecological sensitive spectrum constraint factors, maintaining ecological balance, referring to the CIE standard chromaticity space and combining various factors to construct the spectrum adjustment factor, creating a comfortable visual environment, and using a genetic algorithm to achieve precise spectrum regulation; in the construction of the differential control model, based on reinforcement learning, key elements are integrated, lighting points are controlled in real time according to preset trigger conditions, the model is trained using rich environmental data to adapt to complex scenarios, and the control strategy is continuously optimized through online questionnaire feedback, comprehensively improving the intelligence and humanization level of the lighting system.

[0111] Exemplarily, to better illustrate the corresponding implementation process of the present invention, the following examples are given, including:

[0112] First, the working process of environmental perception:

[0113] By means of lidar and cameras installed at both ends and key positions in the middle of the bridge, the distance between each lighting point and the vehicle (target point) is monitored in real time. Suppose that at a certain moment, the distance between the vehicle closest to the 50th lighting point is 30 meters, and there are 50 vehicles on the bridge at this time;

[0114] Use the sensor to determine the direct light direction of the lighting point corresponding to the vehicle. For example, the included angle between the direct light direction of the 30th lighting point and the vehicle driving direction is 10°;

[0115] The lighting range of each lighting point is set to be a range of 30 meters before and after centered on the lighting point. Further, the lighting point in this embodiment is an LED lamp;

[0116] In this embodiment, the weather conditions and natural light intensity are obtained through a weather station and a light sensor. Suppose it is cloudy at this time and the natural light intensity is 500 lux.

[0117] Second, construct a photosensitive tracking matrix:

[0118] Extract parameters:

[0119] Target area range: The total length of the bridge is 2000 meters and the width is 30 meters. This is the target area range.

[0120] Distribution of lighting points: There is one lighting point every 20 meters on each side, with a total of 200. Clearly define their distribution status.

[0121] Lighting parameters: The lighting power of a single lighting point is 100W, the main beam angle is 120°, the floodlight scattering angle is 180°, the maximum effective illumination distance is 50 meters, and the light attenuation coefficient is determined through simulation experiments. The light intensity is 80 lux at a distance of 10 meters from the lighting point and 40 lux at 20 meters. Based on this, the light attenuation coefficient formula is simulated.

[0122] Spot modeling: By combining the double Gaussian light distribution model with the environmental factor function (considering the influence of the cloudy environment on light scattering), obtain the dynamic lighting spot model and the original dynamic interval of each lighting point. For example, the original dynamic interval of the 10th lighting point in the traveling direction is 20 - 50 meters.

[0123] Calculate the coverage redundancy interval: Based on the lighting spot models of adjacent lighting points (such as the 10th and 11th lighting points) and the maximum lighting interval range, through the integral of the light distribution curve and the Monte Carlo approximation algorithm, obtain their coverage redundancy interval as 30 - 40 meters. Repeat this process to obtain the coverage redundancy interval set of all adjacent lighting points.

[0124] Determine the effective dynamic lighting interval: Taking the 11th lighting point as an example, the lighting spot interval in the traveling direction is 30 - 60 meters. Subtract the product of the overlapping interval length (30 - 40 meters) between the 10th and 11th lighting points and the dynamic attenuation factor (assuming the real-time speed of the target vehicle is 70 km / h and the reference speed is 60 km / h, the dynamic attenuation factor is calculated), and the effective dynamic lighting interval of the 11th lighting point in the traveling direction is 42 - 60 meters.

[0125] Edge light intensity enhancement correction: Input the above effective dynamic lighting interval into the edge light intensity enhancement algorithm to correct the light intensity energy of the subtracted partial interval, and the corrected effective dynamic lighting interval is 40 - 60 meters. Repeat this process to obtain the set of corrected effective dynamic lighting intervals in the same traveling direction.

[0126] Construct the target point movement trajectory prediction data set: Obtain the real-time position, instantaneous speed sequence (such as 70 km / h, 72 km / h, etc.), the distance from the nearest lighting point (the 55th lighting point) in the traveling direction ahead (40 meters), and the system delay corresponding to each lighting point (0.1 second) of a vehicle in the set of effective dynamic lighting intervals, and construct the target point movement trajectory prediction data set.

[0127] Obtain the compensated movement trajectory and real-time position: Input the above target point movement trajectory prediction data set into the compensated movement trajectory function constructed by the support vector machine, and obtain the compensated vehicle movement trajectory and real-time position points according to the actual displacement of the vehicle and the displacement distance corresponding to the lighting delay.

[0128] Determine the set of control time points and construct the photosensitive tracking matrix: Based on the compensated movement trajectory and real-time position points and the set of corrected effective dynamic lighting intervals, obtain the set of control time points corresponding to the lower limit of each corrected effective dynamic lighting interval that the vehicle reaches, and based on this, construct the photosensitive tracking matrix through a 0 - 1 matrix.

[0129] Third, construction of the spectral adjustment factor:

[0130] Divide the lighting time period into two-hour intervals, collect and organize the data on the photosensitive wavelength range (400 - 500 nm has an impact on its vision and foraging) and the physiological function impact of birds (assuming mainly night herons) in the bridge area during different time periods (such as 18:00 - 20:00), and establish a biological-time-photosensitive wavelength database.

[0131] Calculate the overlap degree and the constraint factor: Calculate the overlap degree between the lighting point spectrum and the biological photosensitive wavelength range. For example, the energy proportion of the 400 - 500 nm wavelength band of a certain lighting point spectrum is 0.3, and based on this, obtain the ecological sensitive spectrum constraint factor for the corresponding time period.

[0132] Spectral filtering and correction: Use the ecological sensitivity spectral constraint factor to filter the spectra of each lighting point. According to the CIE standard chromaticity space and the true chromaticity coordinates of the filtered spectra, obtain the required chromaticity deviation, and correct it through the Gaussian attenuation function to obtain the spectrum with completed chromaticity correction.

[0133] Illumination intensity adjustment and function training: Combine the color temperature, color rendering index, glare index, and the temperature (25°C) and humidity (60%) obtained from the meteorological simulation model to construct a visual adaptability adjustment factor to adjust the illumination intensity. Through the genetic algorithm, combine the constraints constructed by the real-time illumination parameters and the required illumination parameters of the lighting points, as well as the ecological sensitivity spectral constraint factor, the visual adaptability adjustment factor, and the spectral adjustment function fitted by the chromaticity deviation to perform training, and obtain the trained spectral adjustment function and weight parameters.

[0134] Fourth, construction of the differential control model:

[0135] Construct a differential control model based on reinforcement learning combined with the spectral adjustment function, the photosensitive tracking matrix, and the preset adjustment trigger conditions. Use the traffic flow (500 vehicles per hour during the morning rush hour, 200 vehicles per hour during the off-peak period), driving speed (average 50 km / h during the morning rush hour, 70 km / h during the off-peak period), weather conditions (sunny, rainy, foggy), and natural light conditions data in different time periods (such as morning and evening rush hours, off-peak periods on weekdays, weekends, etc.) to train the differential control model.

[0136] Trigger conditions and control:

[0137] Natural light control: When the natural light intensity is greater than 500 lux (the preset illumination threshold), all lighting points are turned off; when it is less than this threshold, it enters the silent state.

[0138] Turning on the lighting point and spectral adjustment: When the lighting point is in the silent state, if the 12th lighting point enters the starting point of the effective dynamic lighting interval, it is turned on through the photosensitive tracking matrix, and the spectrum is adjusted through the spectral adjustment function according to the real-time collected lighting point parameters (power, angle, etc.), weather conditions (sunset on a sunny day), and natural light parameters (300 lux).

[0139] Turning off the lighting point: When the lighting point reaches the lower limit of the effective dynamic lighting interval and there is no target point within the interval, such as the 13th lighting point, it is converted to the silent state through the photosensitive tracking matrix.

[0140] Real-time spectral adjustment: When the current lighting point is turned on, the spectrum is repeatedly adjusted according to the target point and lighting point parameters within the interval, such as the 14th lighting point.

[0141] Peak adjustment: When the number of target points in the effective dynamic lighting interval of a certain section (such as the middle area of the bridge) exceeds 30 vehicles per lane (the preset peak threshold), adjust the number of lighting points turned on simultaneously to meet the lighting requirements of all target points.

[0142] Collect driver evaluation information on the current lighting control through an online questionnaire, obtain the regulation evaluation score through a comprehensive evaluation algorithm, construct a reinforcement reward function, and feedback it to the differential control model for continuous training.

[0143] Embodiment 2

[0144] Please refer to Figure 3 , another embodiment provided by the present invention: an LED outdoor lamp environmental adaptability spectrum adjustment system, including: an environmental perception module, an intelligent control module, and a spectrum regulation module;

[0145] The environmental perception module is used to monitor in real time the distance between each lighting point and the target point, the number of target points, the direct illumination direction of the lighting point corresponding to the target point, and the lighting interval within the preset LED lighting array area, and construct a photosensitive tracking matrix according to the distance between each lighting point and the target point, the number of target points, the direct illumination direction of the lighting point corresponding to the target point, and the lighting interval obtained in real time within the preset LED lighting array area. At the same time, according to the real-time environmental parameters of the LED lighting array area and the preset environmental factor function, a spectrum adjustment factor is obtained;

[0146] The intelligent control module controls the turning-on sequence and switching time points of each lighting point corresponding to the target point based on the photosensitive tracking matrix combined with the configured differential control model;

[0147] The spectrum regulation module adjusts the spectrum corresponding to each turned-on lighting point based on the spectrum adjustment factor until the preset standard lighting spectrum obtained based on the CIE standard chromaticity space and the ecological sensitive spectrum under the environment is satisfied, and a synthetic spectrum that meets the requirements of the target point is obtained.

[0148] Furthermore, the working processes corresponding to the environmental perception module, the intelligent control module, and the spectrum regulation module in this embodiment include:

[0149] Through the environmental perception module, in real time monitor the distance between each lighting point and the target point, the number of target points, the direct illumination direction of the lighting point corresponding to the target point, and the lighting interval within the preset LED lighting array area, construct a photosensitive tracking matrix, and use the preset environmental factor function to obtain a spectrum adjustment factor according to the real-time environmental parameters of the LED lighting array area;

[0150] Based on the photosensitive tracking matrix, through the differential control model configured by the intelligent control module, control the turning-on sequence and switching time points of each lighting point corresponding to the target point;

[0151] Meanwhile, based on the spectral adjustment factor, the spectral control module adjusts the spectrum corresponding to each turned-on lighting point until the preset standard lighting spectrum obtained based on the CIE standard chromaticity space and the ecological sensitive spectrum in the environment is satisfied, and a synthesized spectrum meeting the requirements of the target point is obtained.

[0152] The embodiments of the present invention have been described above in conjunction with the accompanying drawings. However, the present invention is not limited to the above specific embodiments. The above specific embodiments are merely illustrative and not restrictive. Under the inspiration of the present invention, those of ordinary skill in the art can also make changes, modifications, substitutions, and variations to the above embodiments without departing from the spirit and scope protected by the claims of the present invention. All of these fall within the protection scope of the present invention.

Claims

1. Method for spectrally adjusting the environmental adaptability of LED outdoor lights, characterized in that, Including: Construct a photosensitive tracking matrix according to the distance between each lighting point and the target point, the number of target points, the direct direction of the lighting point corresponding to the target point, and the lighting interval obtained in real time within the preset LED lighting array area; At the same time, obtain a spectral adjustment factor according to the real-time environmental parameters of the LED lighting array area in combination with the preset environmental factor function; Generate a differential control instruction to control the turning-on sequence and switching time points of each lighting point corresponding to the target point according to the photosensitive tracking matrix in combination with the configured differential control model; While the lighting point is turned on, based on the spectral adjustment factor, adjust the spectrum corresponding to each turned-on lighting point until the preset standard lighting spectrum obtained based on the CIE standard chromaticity space and the ecological sensitive spectrum under the environment is satisfied, and obtain a synthetic spectrum that meets the requirements of the target point; The steps for constructing the photosensitive tracking matrix include: Extract relevant parameters of the target area according to the LED lighting array, model the light spot of the lighting point through the double Gaussian light distribution model in combination with the environmental factor function based on the lighting parameters to obtain the original dynamic interval; then calculate the coverage redundancy interval between adjacent lighting points through the light distribution curve integral and the Monte Carlo approximation algorithm to obtain a set of coverage redundancy intervals; construct a dynamic attenuation factor in combination with the real-time moving speed of the target point, determine the effective dynamic lighting interval and correct it through the edge light intensity enhancement algorithm; then construct a target point movement trajectory prediction data set, input it into the support vector machine to obtain the compensated movement trajectory and real-time position points; finally, determine the control time point set based on the corrected effective dynamic lighting interval set, and construct a photosensitive tracking matrix through a 0-1 matrix; The construction process of the differential control model includes: Based on reinforcement learning, construct a differential control model in combination with the spectral adjustment function, the photosensitive tracking matrix, and the preset adjustment trigger condition, train the model using data on traffic flow, driving speed, weather conditions, and natural light conditions at different time periods, embed the trained model into the corresponding lighting area for real-time LED lighting control, collect target point evaluation information through an online questionnaire, obtain a regulation evaluation score through comprehensive evaluation, and construct a reinforcement reward function to feedback to the differential control model for continuous training; The process of obtaining the spectral adjustment factor includes: Divide the lighting time period into two-hour time periods. Based on the ecologically sensitive spectrum, collect and organize the photosensitive band ranges of all relevant organisms in the lighting area and the corresponding physiological function impact data for each time period. Classify and store the collected photosensitive band ranges to establish a biological-time-photosensitive band database. According to the spectrum of the corresponding lighting point and the biological-time-photosensitive band database, obtain the overlap degree of each biological photosensitive band. Based on the overlap degree of each biological photosensitive band, obtain the ecologically sensitive spectrum constraint factor for each time period. Use the obtained ecologically sensitive spectrum constraint factor to filter the spectrum corresponding to each lighting point according to the target required spectrum to obtain the filtered spectrum. Based on the CIE standard colorimetric space in the environment and the true chromaticity coordinates corresponding to the filtered spectrum of the real-time lighting point, obtain the required chromaticity deviation. Based on the required chromaticity deviation, correct the chromaticity deviation through a Gaussian attenuation function to obtain the spectrum with corrected chromaticity. Based on the spectrum with corrected chromaticity, construct a visual adaptability adjustment factor through the color temperature, color rendering index, glare index, and temperature and humidity obtained from the meteorological simulation model, and adjust the light intensity of the spectrum with corrected chromaticity to obtain the spectrum with adjusted light intensity. According to the obtained ecologically sensitive spectrum constraint factor, visual adaptability adjustment factor, and chromaticity correction process, train through the constraint conditions constructed by the genetic algorithm combined with the real-time lighting parameters and required lighting parameters of the lighting point, and the spectrum adjustment function fitted by the ecologically sensitive spectrum constraint factor, visual adaptability adjustment factor, and chromaticity deviation to obtain the trained spectrum adjustment function and the corresponding weight parameters, thereby obtaining the spectrum adjustment factor.

2. The method for adjusting the environmental adaptability spectrum of the LED outdoor lamp according to claim 1, characterized in that, The steps for constructing the photosensitive tracking matrix include: Extract the range interval of the target area, the distribution state of the lighting points in the target area, the number of lighting points, and the lighting parameters of a single lighting point from the LED lighting array; Based on the light power, main beam angle and floodlight scattering angle, maximum effective illumination distance, and light attenuation coefficient corresponding to the lighting parameters of each lighting point, perform lighting point spot modeling through a double Gaussian light distribution model combined with an environmental factor function to obtain the dynamic lighting spot model corresponding to each lighting point and the original dynamic interval of the corresponding lighting spot in the traveling direction; The light attenuation coefficient is obtained through a simulation experiment based on the characteristic relationship between light power, light intensity, and distance; Based on the dynamic lighting spot models corresponding to two adjacent lighting points in the same traveling direction and the maximum lighting interval ranges corresponding to the two lighting points, obtain the coverage redundancy interval corresponding to two adjacent lighting points in the same traveling direction through light distribution curve integration and the Monte Carlo approximation algorithm; Repeat the above process to obtain the coverage redundancy intervals corresponding to all adjacent lighting points and obtain a set of coverage redundancy intervals.

3. The method for spectrally adjusting the environmental adaptability of the LED outdoor lamp according to claim 2, wherein, The steps for constructing the photosensitive tracking matrix also include: Subtract the product of the interval length of the coverage redundancy interval corresponding to the first and second illumination points in the same traveling direction and the dynamic attenuation factor from the interval of the illumination spot corresponding to the second illumination point in the traveling direction to obtain the effective dynamic illumination interval of the second illumination point in the traveling direction; Input the effective dynamic illumination interval of the second illumination point in the traveling direction into the edge light intensity enhancement algorithm to correct the light intensity energy of the subtracted partial interval, and obtain the corrected effective dynamic illumination interval of the second illumination point in the traveling direction; The dynamic attenuation factor is constructed through the real-time moving speed of the target point and the reference moving speed; Repeat the process of obtaining the effective dynamic illumination interval of the second illumination point in the traveling direction to obtain a set of corrected effective dynamic illumination intervals in the same traveling direction.

4. The method for adjusting the environmental adaptability spectrum of the LED outdoor lamp according to claim 3, wherein, The steps of constructing the photosensitive tracking matrix further include: Obtain the real-time position, instantaneous velocity sequence, distance from the nearest illumination point in the front of the traveling direction, and system delay corresponding to each illumination point of the target point in the set of effective dynamic illumination intervals, and construct a target point movement trajectory prediction data set; Input the target point movement trajectory prediction data set into the compensation movement trajectory function constructed by the support vector machine, and obtain the compensated movement trajectory and real-time position points corresponding to the target point according to the actual displacement of the target point and the displacement distance corresponding to the illumination point delay; Based on the compensated movement trajectory and real-time position points corresponding to the target point and the set of corrected effective dynamic illumination intervals in the same traveling direction, obtain the set of control time points corresponding to each target point reaching the lower limit of each corrected effective dynamic illumination interval.

5. The method for spectrally adjusting the environmental adaptability of an LED outdoor lamp according to claim 4, characterized in that, The steps of constructing the photosensitive tracking matrix further include: Based on the set of control time points, a photosensitive tracking matrix is constructed through a 0-1 matrix , specifically: ; Among them, represents the illumination-tracking factor of the i-th target point in the corrected effective dynamic illumination interval corresponding to the j-th illumination point, represents the real-time running time length of the i-th target point in the corrected effective dynamic illumination interval corresponding to the j-th illumination point, represents the time length when the i-th target point runs to the lower limit of the interval in the corrected effective dynamic illumination interval corresponding to the j-th illumination point, represents the switch delay time length corresponding to the j-th illumination point.

6. The method for adjusting the environmental adaptability spectrum of the LED outdoor lamp according to claim 5, wherein, The preset adjustment trigger conditions include: When the natural light intensity is greater than the preset illumination threshold, all illumination points are in the off state, otherwise, all illumination points are in the silent state; When all illumination points are in the silent state, when at least one target point enters the starting position point of the effective dynamic illumination interval, turn on the corresponding illumination point through the photosensitive tracking matrix, and synchronously adjust the spectrum of the turned-on illumination point through the spectrum adjustment function according to the real-time collected illumination point parameters, weather condition parameters, and natural light parameters; When the target point reaches the position point corresponding to the lower limit of the effective dynamic illumination interval and there is no target point in the current effective dynamic illumination interval, then convert the corresponding illumination point into the silent state through the photosensitive tracking matrix; Otherwise, the current illumination point is in the on state, and according to the target point and illumination point parameters in the corresponding effective dynamic illumination interval, repeat the above process to adjust the spectrum of the current illumination point in real time; When the number of target points corresponding to at least one effective dynamic illumination interval exceeds the preset peak threshold, adjust the number of simultaneously turned-on illumination points so that the corresponding light intensity simultaneously meets the light requirements corresponding to all target points.

7. An LED outdoor light environmental adaptability spectrum adjustment system, which is used to implement the LED outdoor light environmental adaptability spectrum adjustment method described in any one of claims 1-6, and is characterized in that, Including: An environment perception module, an intelligent control module, and a spectrum regulation module; The environmental perception module is used to construct a photosensitive tracking matrix based on the distance between each lighting point and the target point, the number of target points, the direct illumination direction of the lighting point corresponding to the target point, and the lighting interval, which are real-time obtained within the preset LED lighting array area. Meanwhile, according to the real-time environmental parameters of the LED lighting array area and in combination with the preset environmental factor function, a spectral adjustment factor is obtained; The intelligent control module controls the turning-on sequence and switching time points of each lighting point corresponding to the target point based on the photosensitive tracking matrix in combination with the configured differential control model; The spectral regulation module adjusts the spectrum corresponding to each turned-on lighting point based on the spectral adjustment factor until the preset standard illumination spectrum obtained based on the CIE standard chromaticity space and the ecological sensitive spectrum under the environment is satisfied, and a synthesized spectrum that meets the requirements of the target point is obtained.

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