High-luminous-efficiency LED street lamp based on multi-chip combination and luminous flux improving method thereof

Abstract

The invention relates to the field of electrical engineering, and discloses a high-luminous-efficiency LED street lamp based on multi-chip combination and a luminous flux improving method thereof, and the method comprises the steps: obtaining photoelectric parameter data, carrying out the classification modeling of chips, and constructing a light source feature database; self-adaptive parameter extraction is carried out on the light source feature database, a cooperative work model is constructed, the chip load state is adjusted through a current sharing control strategy, and the chip heat distribution change condition is monitored in real time; whether the working state is stable or not is judged based on the heat distribution change condition, and if the working state is stable, stage change data of luminous flux output is recorded, and a chip driving strategy is corrected; according to the corrected chip driving strategy, spatial distribution regulation and control and energy efficiency optimization are carried out on output luminous flux, regulation factors are extracted, a light emitting path and heat dissipation intensity are adjusted, and luminous efficiency balance improvement is carried out; and evaluating the luminous flux output effect based on the luminous efficiency balance improvement result, and generating a working state evaluation report and a luminous flux improvement suggestion.

Description

Technical Field

[0001] The present invention relates to the field of electrical engineering technology, and in particular to a high-light-efficiency LED street lamp based on a multi-chip combination and a method for increasing the luminous flux thereof. Background Art

[0002] In the current LED street light technology, LED light sources based on multi-chip combinations have been widely used to improve luminous efficiency and achieve high-power lighting. However, there are still some subtle but critical problems in the existing technology, which affect the further improvement of luminous flux. Specifically, in the multi-chip packaging process, the uneven heat distribution and inconsistent current distribution between different chips are still prominent. This imbalance will cause some chips to be in an overheated or overloaded state, thereby reducing their luminous efficiency and service life, and ultimately affecting the luminous flux output stability and overall efficiency of the entire LED module. The traditional single photoelectric parameter screening mode is complex to process and it is difficult to capture the transient performance changes of the chip. It is difficult to balance the light color consistency and thermal matching in the traditional combination method. The traditional single physical field analysis has limitations. Therefore, there is a need to improve the luminous flux performance of LED street lights based on multi-chip combinations and luminous flux improvement methods;

[0003] In view of this, it is necessary to provide a high-light-efficiency LED street lamp based on a multi-chip combination and a method for improving the luminous flux thereof. Summary of the Invention

[0004] The purpose of the present invention is to provide a high-light-efficiency LED street lamp based on a multi-chip combination and a method for increasing the luminous flux thereof. To solve the above-mentioned problems in the prior art, the present invention is achieved through the following technical solutions:

[0005] An embodiment of the present invention provides a method for increasing the light flux of a high-efficiency LED street light based on a multi-chip combination, which specifically includes the following steps:

[0006] Step 1: Obtain photoelectric parameter data, combine chip performance grading algorithm and equivalent thermal resistance model, classify and model the chips, and build a light source feature database;

[0007] Step 2: Adaptively extract parameters from the light source feature database, build a collaborative working model through a coupled optimization mechanism, adjust the chip load state through a current sharing control strategy, and monitor the chip thermal distribution changes in real time;

[0008] Step 3: Based on the changes in thermal distribution, determine whether the working state is stable. If stable, record the staged change data of the luminous flux output and modify the chip driving strategy in combination with changes in the operating environment;

[0009] Step 4: Based on the revised chip driving strategy, the spatial distribution of the output luminous flux is regulated and the energy efficiency is optimized. The adjustment factor is extracted, and the light output path and heat dissipation intensity are adjusted to achieve a balanced improvement in light efficiency.

[0010] Step 5: Based on the results of the balanced improvement of light efficiency, evaluate the luminous flux output effect and generate a working status assessment report and luminous flux improvement suggestions.

[0011] Furthermore, the specific implementation method of the classification modeling is:

[0012] The photoelectric parameter data of a single chip under constant current drive is measured and collected by using an integrating sphere spectroscopy system;

[0013] In a constant temperature environment, perform N continuous full-power scans on each chip, remove outliers and take the average as the baseline data to generate the initial parameter matrix X b :

[0014] X b =[V f ,I op ,Φ,CCT,RA,T j ,R th ]

[0015] Among them, V f is the forward voltage, I op is the driving current, Φ is the luminous flux, CCT is the color temperature, RA is the color rendering index, T j is the junction temperature, R th is the thermal resistance;

[0016] Based on the obtained driving current, the driving current fluctuation step change is analyzed, and the ratio of the luminous flux change to the change time is synchronously collected to obtain the luminous flux attenuation rate. The ratio of the junction temperature change to the change time is collected to obtain the junction temperature rise rate. The data is denoised through Kalman filtering to generate dynamic response characteristics;

[0017] Based on the obtained photoelectric parameter data, a three-level classification modeling system is constructed to achieve deep integration and classification of photoelectric and thermal parameters;

[0018] Furthermore, the method for obtaining the three-level classification modeling system is:

[0019] Perform primary classification through photoelectric performance screening and extract photoelectric performance indicators based on photoelectric parameter data;

[0020] The voltage fluctuation is obtained by obtaining the maximum and minimum values ​​of the forward voltage and calculating the difference. The voltage stability δ is calculated by calculating the ratio of the voltage fluctuation to the average forward voltage. V ;

[0021] The voltage stability is compared with a preset stability standard to determine whether the photoelectric performance of the chip is qualified, and if the voltage stability exceeds the preset stability standard, the corresponding chip is determined as unqualified.

[0022] A Gaussian mixture model is used to model the joint distribution of the light efficiency grade and the voltage stability, and the posterior probability is calculated to obtain the primary classification score to determine the initial classification, and the posterior probability obtained through the formula is the primary classification score.

[0023] The thermal stability is corrected to perform the intermediate classification, and a thermal resistance correction factor a is introduced th The primary classification score is corrected, and the thermal stability obtained through the formula is the intermediate classification score.

[0024] The highest temperature value reached by the chip and the initial temperature value when the chip starts working are obtained, and the thermal runaway risk coefficient is calculated through the formula;

[0025] Based on the obtained thermal runaway risk coefficient, the thermal runaway risk of the chip is analyzed and determined;

[0026] The chip combination matching degree between chips is optimized, and the chip combination matching degree after iteration is calculated through the construction of a target function by using a NSGA-II multi-objective genetic algorithm;

[0027] Further, the specific implementation method for adaptive parameter extraction is:

[0028] A Gaussian kernel function is used The nonlinear features are mapped, where σ is a function bandwidth parameter, σ = 0.5, the photoelectric parameter data is mapped from a low-dimensional space to a high-dimensional feature space, and then the key features are extracted through principal component analysis; the principal components with a cumulative contribution rate of ≥95% are retained, the color difference of the chip corresponding to the LED lamp is obtained, and a feature weight vector is generated, corresponding to the light efficiency grade, thermal stability, and color difference, respectively.

[0029] Further, the specific implementation method for constructing the cooperative working model is:

[0030] A thermal-electric-optical multi-physical field cooperative working model is constructed based on the photoelectric parameter data, and the thermal-electric-optical multi-physical field equation set

[0031]

[0032] The cooperative working model is constructed.

[0033] Wherein, is the partial derivative of temperature T with respect to time t, is the Laplacian of temperature T, a is the thermal diffusivity, c p is the specific heat capacity of the material, p is the material density, η(T) is the light efficiency grade when the temperature is T, η0 is the initial light efficiency grade at a preset normal temperature, and Φtotal is the total luminous flux, θ i is the light output angle offset of the i-th LED lamp, i is the LED lamp index, n is the total number of LED lamps, η i (T j ) is the junction temperature T j The corresponding light effect level, P i is the power of the i-th LED lamp;

[0034] Furthermore, the specific implementation method of adjusting the chip load state is:

[0035] Based on the construction of collaborative working model, the chip load status is dynamically adjusted and a dual closed-loop control strategy is designed:

[0036] Inner loop: distributes current based on LM5069 chip;

[0037] Outer loop: Through the fuzzy neural network FNN controller, the input is the temperature gradient and current imbalance, and the output is pulse width modulation PWM regulation;

[0038] Deploy a 5×5 thermocouple array, use Kalman filtering to reduce noise, generate a heat distribution cloud map and use the formula:

[0039]

[0040] The thermal uniformity index HUI is calculated;

[0041] Furthermore, the stability state of the system is determined by defining the working stability value, obtaining the junction temperature and driving current of the LED lamp within the sampling period, calculating the standard deviation of the junction temperature and the standard deviation of the driving current within the sampling period, and obtaining the working stability value through the formula;

[0042] Based on the obtained working stability value, determine whether the system stability state is stable:

[0043] Furthermore, the specific implementation method of the modified chip driving strategy is:

[0044] Based on the driving current and the ambient temperature and humidity, the driving current is corrected by adaptive compensation, using the correction formula:

[0045] I new =I base *[1+0.02(T e -25)-0.005RH]*(1-0.0001t)

[0046] Get the corrected driving current I new ;

[0047] Among them, I base is the basic driving current, Te Ambient temperature, RH is ambient humidity percentage;

[0048] Further, the specific implementation method of the space distribution regulation and energy efficiency optimization is:

[0049] The target illumination value is obtained by adopting free-form surface secondary optical design technology and establishing a target illumination distribution function;

[0050] Based on the obtained target illumination value, the reflective surface is optimized by using non-sequential ray tracing technology, the non-sequential ray tracing simulates the propagation path of light in a complex optical system, and the curvature radius of the reflective surface is adjusted until the actual light distribution meets the requirements set by the target illumination distribution function, i.e., the target illumination value;

[0051] Further, the specific implementation method of the light efficiency balance improvement is:

[0052] Based on the obtained light efficiency grade and junction temperature, the light efficiency balance is optimized by using a particle swarm algorithm, a light efficiency gain factor is defined, and the formula is:

[0053]

[0054] The light efficiency gain factor K is calculated;

[0055] Wherein, η new is the optimized light efficiency grade, η old is the initial light efficiency grade, is the initial maximum junction temperature, is the optimized maximum junction temperature;

[0056] The light efficiency grade is improved based on the obtained light efficiency gain factor.

[0057] The beneficial effects of the present application are:

[0058] 1. Fusion of photoelectric parameters, thermal characteristics and dynamic response characteristics, breakthrough of traditional single photoelectric parameter screening mode; introduction of driving current fluctuation step analysis, capture of chip transient performance change through Kalman filter processing of luminous flux decay rate and junction temperature rise rate; construction of multi-objective function including light efficiency grade, wavelength difference, junction temperature and voltage balance; optimal chip combination is solved through genetic algorithm iteration, which solves the problem that light color consistency and thermal matching are difficult to consider in traditional combination mode; the coupling relationship between temperature field distribution, light efficiency decay and luminous flux is calculated by coupling temperature field distribution, light efficiency decay and luminous flux, the dynamic influence of temperature on light efficiency and multi-chip light field superposition effect in the formula, cross-scale modeling from chip level to system level is realized, and the limitation of traditional single physical field analysis is broken through;

[0059] 2. The inner ring realizes accurate distribution of current, the outer ring dynamically adjusts PWM pulse width through fuzzy rules, and the thermal distribution feedback is constructed by combining thermocouple array and Kalman filter; the temperature and humidity parameters are included in the driving current correction model to compensate the influence of environmental changes on light efficiency in real time; based on the target illumination distribution function and non-sequential ray tracing technology, the uniformity of street lamp illumination and light efficiency are cooperatively optimized to improve luminous flux; an evaluation system including light efficiency improvement rate, thermal uniformity index, illumination root mean square error and junction temperature variation coefficient is constructed, and the full-link performance monitoring is realized through data visualization. BRIEF DESCRIPTION OF DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0061] Figure 1 is a step flow chart of a high light efficiency LED street lamp luminous flux improvement method based on multi-chip combination provided by the embodiment 1 of the present application;

[0062] Figure 2 is a step flow chart of judging whether the working state is stable in the high light efficiency LED street lamp luminous flux improvement method based on multi-chip combination provided by the embodiment 1 of the present application. DETAILED DESCRIPTION

[0063] In order to make the person skilled in the art better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely in the following with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should be within the scope of protection of the present application.

[0064] Embodiment 1

[0065] As shown in Figure 1 , the high light efficiency LED street lamp luminous flux improvement method based on multi-chip combination provided by the embodiment of the present application specifically includes the following steps:

[0066] Step 1: Obtain photoelectric parameter data, classify and model the chips by combining chip performance grading algorithm and equivalent thermal resistance model, and construct light source feature database;

[0067] In a specific embodiment, the specific implementation method of the classification and modeling is:

[0068] The photoelectric parameter data of a single chip under constant current drive is measured and collected by using an integrating sphere spectroscopy system. The photoelectric parameter data includes: electrical parameters, optical parameters and thermal parameters;

[0069] It should be noted that the electrical parameters include: forward voltage V f , driving current I op and power P; optical parameters include: luminous flux Φ, color temperature CCT and color rendering index RA; thermal parameters include: junction temperature T j , heat sink temperature T s and thermal resistance R th ;

[0070] During the initial testing phase of the integrating sphere spectroscopy system, all parameters are sampled at high frequency to obtain the initial performance baseline of the chip;

[0071] During the operation phase of the integrating sphere spectroscopy system, dynamic sampling is performed, focusing on monitoring the real-time fluctuations of junction temperature and luminous flux;

[0072] The thermal resistance parameters are obtained through transient thermal testing method, and the relationship curve between junction temperature and heat sink temperature is recorded;

[0073] Specifically, the luminous flux and spectral distribution are measured by a photodetector, the chip junction temperature and thermal field distribution are captured by an infrared thermal imager, and the color temperature and color rendering index are analyzed by a spectrometer.

[0074] In a constant temperature environment, perform N continuous full-power scans on each chip, remove outliers and take the average as the baseline data to generate the initial parameter matrix X b :

[0075] X b =[V f ,I op ,Φ,CCT,RA,T j ,R th ]

[0076] Based on the obtained driving current, the driving current fluctuation step change is analyzed. If the driving current fluctuation step change exceeds 10%, the ratio of the luminous flux change to the change time is synchronously collected to obtain the luminous flux attenuation rate. The ratio of the junction temperature change to the change time is collected to obtain the junction temperature rise rate. The data is denoised by Kalman filtering to generate the dynamic response feature D. d :

[0077]

[0078] in, is the rate of change of luminous flux to driving current, is the rate of change of junction temperature to power, σ CCT is the color temperature standard deviation;

[0079] Based on the obtained photoelectric parameter data, a three-level classification modeling system is constructed to achieve deep integration and classification of photoelectric and thermal parameters;

[0080] Primary classification is performed through photoelectric performance screening, and photoelectric performance indicators are extracted based on photoelectric parameter data. Photoelectric performance indicators include: light efficiency level and electrical stability;

[0081] Specifically, the light efficiency level η is obtained by performing ratio processing on the luminous flux and the power, and the obtained light efficiency level is compared with the level threshold to classify the light efficiency level into three levels: A, B, and C.

[0082] The voltage fluctuation is obtained by obtaining the maximum and minimum values ​​of the forward voltage and calculating the difference. The voltage stability δ is calculated by calculating the ratio of the voltage fluctuation to the average forward voltage. V ;

[0083] Compare the voltage stability with the preset stability standard to determine whether the chip's optoelectronic performance is qualified. If the voltage stability exceeds the preset stability standard, the corresponding chip is judged to be unqualified.

[0084] A Gaussian mixture model is used to model the joint distribution of light efficiency level and voltage stability, and the posterior probability is calculated to obtain the primary classification score to determine the initial classification. The formula is:

[0085]

[0086] Get the posterior probability P(class=k|η,δ V ) is the primary classification score, where π k is the category prior probability, μ k is the mean vector, Σ k is the covariance matrix, is the Gaussian distribution density probability density function, i is the category index;

[0087] Intermediate classification is performed through thermal stability correction, and thermal resistance correction factor α is introduced th Corrected primary classification score, by the formula:

[0088]

[0089] Get the thermal stability, which is the intermediate classification score Score m ;

[0090] Among them, R th is the chip thermal resistance, R th_avg is the average thermal resistance of chips in the same batch, R th_std is the standard deviation of thermal resistance of chips in the same batch;

[0091] If the intermediate classification score drops by more than 15%, the chip is downgraded. For example, if the intermediate classification score of a grade A chip drops by more than 15%, the chip is downgraded from grade A to grade B.

[0092] Get the highest temperature value reached by the chip and the initial temperature value when the chip starts working, through the formula:

[0093]

[0094] The thermal runaway risk coefficient Risk is calculated;

[0095] in, is the maximum junction temperature of the chip, is the initial junction temperature of the chip, t risk is the heating time, R th is the chip thermal resistance;

[0096] The chip's thermal runaway risk is analyzed and determined based on the obtained thermal runaway risk coefficient. If the thermal runaway risk coefficient is greater than the preset risk coefficient standard value, the corresponding chip is downgraded to the lowest level, Class C.

[0097] Optimize the chip combination matching between chips and construct the objective function through the NSGA-II multi-objective genetic algorithm

[0098]

[0099] The chip combination matching degree F after iteration is calculated, the population size is 200, the crossover rate is 0.8, and the iteration termination condition is that the chip combination matching degree difference ΔF between adjacent iterations is less than 0.1%;

[0100] Among them, n is the number of chips participating in the combination, i is the chip index, η1 is the light efficiency level of the i-th chip, j is the chip index, Δλ ij is the wavelength difference between the i-th chip and the j-th chip, is the highest junction temperature of the i-th chip, T0 is the highest standard working junction temperature, V i is the operating voltage of the i-th chip, V j is the operating voltage of the jth chip, ΔV0 is the voltage standard deviation;

[0101] It should be noted that the NSGA-II multi-objective genetic algorithm is a multi-objective optimization algorithm used to solve the above multi-objective optimization problem. By simulating the natural evolution process, it searches for a chip combination solution that maximizes the objective function F and satisfies the constraints.

[0102] In a specific embodiment, the specific implementation method of constructing the light source feature database is:

[0103] The obtained optoelectronic parameter data, primary classification scores, intermediate classification scores, thermal runaway risk coefficients, and iterative chip combination matching degrees of all chips are uniformly stored according to the chip number or identification. A database management system is used to implement structured storage of data, adding detailed annotation information to the stored data. The optoelectronic parameter data of the chips is collected regularly, and the classification information and related feature data of the chips are updated.

[0104] Step 2: Adaptively extract parameters from the light source feature database, build a collaborative working model through a coupled optimization mechanism, adjust the chip load state through a current sharing control strategy, and monitor the chip thermal distribution changes in real time;

[0105] In a specific embodiment, the specific implementation method of performing the adaptive parameter extraction is:

[0106] Using Gaussian kernel function The nonlinear features are mapped, where σ is the function bandwidth parameter and σ = 0.5. The photoelectric parameter data is mapped from the low-dimensional space to the high-dimensional feature space, and then the key features are extracted through principal component analysis. The principal components with a cumulative contribution rate of ≥ 95% are retained. The color difference of the LED light corresponding to the chip is obtained, and the feature weight vector W = [0.55, 0.30, 0.15] is generated, corresponding to the light efficiency level, thermal stability, and color difference, respectively.

[0107] In a specific embodiment, the specific implementation method of building a collaborative work model is:

[0108] Based on the photoelectric parameter data, a thermal-electric-optical multi-physics field collaborative working model is constructed.

[0109]

[0110] Build a collaborative working model;

[0111] in, is the partial derivative of temperature T with respect to time t, is the Laplace operator of temperature T, which describes the second-order change of temperature in space and reflects the distribution trend of temperature in space. α is the thermal diffusivity, α = 4.5×10 -5 , c p is the material specific heat capacity, ρ is the material density, η(T) is the light efficiency level at temperature T, η0 is the initial light efficiency level at the preset room temperature, Φ total is the total luminous flux, θ i is the light output angle offset of the i-th LED lamp, i is the LED lamp index, n is the total number of LED lamps, η i (T j ) is the junction temperature T j The corresponding light effect level, Pi is the power of the i-th LED lamp;

[0112] In a specific embodiment, the specific implementation method of adjusting the chip load state is:

[0113] Based on the construction of collaborative working model, the chip load status is dynamically adjusted and a dual closed-loop control strategy is designed:

[0114] Inner loop: Based on the LM5069 chip, it realizes accurate current distribution with a resolution of 0.1mA.

[0115] Outer loop: Through the fuzzy neural network FNN controller, the input is the temperature gradient ΔT and the current imbalance δ I , output pulse width modulation PWM adjustment amount, the rule base contains fuzzy rules;

[0116] For example, if the temperature variation is at a high variation level and the current fluctuation is at a medium variation level, the pulse width modulation (PWM) adjustment amount PWM adj PWM adj =-0.12*ΔT+0.08δ I ;

[0117] Deploy a 5×5 thermocouple array, use Kalman filtering to reduce noise, generate a heat distribution cloud map and use the formula:

[0118]

[0119] The thermal uniformity index (HUI) is calculated and the thermal uniformity index target is preset to: HUI ≥ 0.92;

[0120] Step 3: Based on the changes in thermal distribution, calculate the working stability value to determine whether the working state is stable, record the staged change data of the luminous flux output, and modify the chip driving strategy in combination with the changes in the use environment;

[0121] In a specific embodiment, the specific implementation method of calculating the working stability value is:

[0122] By defining the working stability value to judge the stability state of the system, the junction temperature T of the LED lamp within the sampling period is obtained. j With the driving current I op , calculate the standard deviation of junction temperature σ during the sampling period T and the standard deviation of the driving current σ I , through the formula:

[0123]

[0124] Calculate the working stability value S;

[0125] Among them, Iavg is the average driving current;

[0126] Based on the obtained working stability value S, the stability of the system is judged. If the working stability value is less than the preset working stability standard value and lasts for 10 sampling cycles, the system is determined to be in a stable state and the phased change data of the luminous flux output is recorded. Otherwise, the adjustment mechanism will be triggered to record the phased change data of the luminous flux output and modify the chip driving strategy to make it tend to be stable.

[0127] In a specific embodiment, the specific implementation method of the modified chip driving strategy is:

[0128] Based on the driving current and the ambient temperature and humidity, the driving current is corrected by adaptive compensation, using the correction formula:

[0129] I new =I base *[1+0.02(T e -25)-0.005RH]*(1-0.0001t)

[0130] Get the corrected driving current I new ;

[0131] Among them, I base is the basic driving current, T e is the ambient temperature, RH is the ambient humidity percentage;

[0132] Step 4: Based on the revised chip driving strategy, the spatial distribution of the output luminous flux is regulated and the energy efficiency is optimized. The adjustment factor is extracted, and the light output path and heat dissipation intensity are adjusted to achieve a balanced improvement in light efficiency.

[0133] In a specific embodiment, the specific implementation method of the spatial distribution control and energy efficiency optimization is:

[0134] Using free-form surface secondary optical design technology, the target illumination distribution function is established:

[0135]

[0136] Get the target illumination value E(x,y);

[0137] Among them, E0 is the preset reference illumination value, (x, y) is the spatial coordinate of different positions, x c is the horizontal coordinate of the target illumination distribution, y c is the ordinate of the target illumination distribution, σ is the distribution parameter related to the road width, and the preset value is 0.7;

[0138] Based on the obtained target illuminance value, the reflective surface is optimized using non-sequential ray tracing technology. Non-sequential ray tracing simulates the propagation path of light in a complex optical system and continuously adjusts the curvature radius of the reflective surface until the actual light distribution reaches the target illuminance value set by the target illuminance distribution function.

[0139] In a specific embodiment, the specific implementation method for improving the light efficiency balance is:

[0140] Based on the obtained light efficiency level and junction temperature, the particle swarm algorithm is used to optimize the light efficiency balance and define the light efficiency gain factor through the formula:

[0141]

[0142] The light efficiency gain factor K is calculated;

[0143] Among them, η new is the optimized light efficiency level, η old is the initial light efficiency level, is the initial maximum junction temperature, is the maximum junction temperature after optimization;

[0144] Based on the obtained light efficiency gain factor, the light efficiency level is improved in a balanced manner;

[0145] Step 5: Based on the results of the light efficiency balance improvement, evaluate the luminous flux output effect and generate a working status evaluation report and luminous flux improvement suggestions;

[0146] In a specific embodiment, the specific implementation method of evaluating the luminous flux output effect is:

[0147] Analyze the luminous flux output effect of street lamps based on the photoelectric parameter data of street lamps;

[0148] Specifically, the light efficiency level difference is obtained by calculating the difference between the light efficiency level before optimization and the light efficiency level after optimization, and the light efficiency improvement rate is obtained by performing a ratio processing on the light efficiency level difference and the initial light efficiency level;

[0149] The thermal field distribution is collected by a thermocouple array to obtain the thermal uniformity index (HUI). The HUI is required to be ≥ 0.92. If it is lower than the preset index threshold, it is judged as uneven heat dissipation.

[0150] Based on the target illuminance distribution function, calculate the root mean square error between the measured illuminance value and the target illuminance value;

[0151] By formula The illumination uniformity U is calculated and required to be ≥ 0.85 in the road lighting scene;

[0152] Calculate the ratio of the standard deviation of the junction temperature to the average junction temperature over 10 consecutive sampling periods to obtain the junction temperature variation coefficient. If the junction temperature variation coefficient is greater than the preset junction temperature fluctuation value, it is determined that the junction temperature fluctuation is abnormal, and the heat dissipation efficiency is improved;

[0153] Fit the luminous flux attenuation curve over time and calculate the luminous flux attenuation per unit sampling period. The luminous flux attenuation is required to be less than or equal to the standard luminous flux attenuation.

[0154] Generates a working status assessment report based on the luminous flux output effect, including a real-time thermal field cloud map, light efficiency improvement curve, energy efficiency comparison chart, chip classification information, such as the proportion of A-grade chips, and annotates abnormal parameters;

[0155] Output optimization suggestions based on the work status assessment report;

[0156] Specifically, if the light efficiency improvement rate is less than 5%, the chip combination matching is re-optimized and the wavelength difference weight is adjusted using the NSGA-II algorithm;

[0157] If the thermal uniformity HUI is less than 0.92 or the junction temperature variation coefficient is greater than the preset junction temperature fluctuation value, the heat dissipation intensity adjustment mechanism is triggered to dynamically adjust the fan power based on the junction temperature variation coefficient;

[0158] If the spatial illumination uniformity U is less than 0.85, the curvature of the free-form reflective surface is re-optimized, and the non-sequential ray tracing technology is used to increase the number of iterations.

[0159] An embodiment of the present invention is described in detail above, but the content described is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention; the above formulas are all dimensionless and numerical calculations, and the formula is a formula for the most recent real situation obtained by collecting a large amount of data and performing software simulation. The preset parameters in the formula are set by technicians in this field based on actual conditions and historical experience, and can be adjusted according to actual conditions; the above description is only a preferred embodiment of the present invention and is not used to limit the present invention. All equal changes and improvements made according to the scope of application of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination, characterized in that: The following steps are involved: Obtain photoelectric parameter data, combine chip performance grading algorithm and equivalent thermal resistance model to classify and model the chips, and build a light source feature database; Adaptive parameter extraction is performed on the light source feature database. A collaborative working model is constructed through a coupled optimization mechanism. The chip load state is adjusted through a current sharing control strategy, and changes in the chip thermal distribution are monitored in real time. Based on the changes in thermal distribution, determine whether the working state is stable. If stable, record the stage-by-stage change data of the luminous flux output and modify the chip driving strategy in combination with changes in the operating environment. Based on the revised chip driving strategy, the spatial distribution of the output luminous flux is regulated and the energy efficiency is optimized. The adjustment factor is extracted, the light output path and heat dissipation intensity are adjusted, and the light efficiency is balanced and improved. Based on the results of balanced light efficiency improvement, the luminous flux output effect is evaluated, and a working status assessment report and luminous flux improvement suggestions are generated.

2. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method of the classification modeling is: The photoelectric parameter data of a single chip under constant current drive is measured and collected by using an integrating sphere spectroscopy system; In a constant temperature environment, perform N continuous full-power scans on each chip, remove outliers and take the average as the baseline data to generate the initial parameter matrix X b : X b =[V f ,I op ,Φ,CCT,RA,T j ,R th ] Among them, V f is the forward voltage, I op is the driving current, Φ is the luminous flux, CCT is the color temperature, RA is the color rendering index, T j is the junction temperature, R th is the thermal resistance; Based on the obtained driving current, the driving current fluctuation step change is analyzed, and the ratio of the luminous flux change to the change time is synchronously collected to obtain the luminous flux attenuation rate. The ratio of the junction temperature change to the change time is collected to obtain the junction temperature rise rate. The data is denoised through Kalman filtering to generate dynamic response characteristics; Based on the obtained photoelectric parameter data, a three-level classification modeling system is constructed to achieve deep integration and classification of photoelectric and thermal parameters.

3. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 2, characterized in that: The method for obtaining the three-level classification modeling system is: Perform primary classification through photoelectric performance screening and extract photoelectric performance indicators based on photoelectric parameter data; The voltage fluctuation is obtained by obtaining the maximum and minimum values ​​of the forward voltage and calculating the difference. The voltage stability δ is calculated by calculating the ratio of the voltage fluctuation to the average forward voltage. V ; Compare the voltage stability with the preset stability standard to determine whether the chip's optoelectronic performance is qualified. If the voltage stability exceeds the preset stability standard, the corresponding chip is judged to be unqualified. A Gaussian mixture model is used to model the joint distribution of light efficiency level and voltage stability. The posterior probability is calculated to obtain the primary classification score to determine the initial classification. The posterior probability obtained by the formula is the primary classification score. Intermediate classification is performed through thermal stability correction, and thermal resistance correction factor α is introduced th Correct the primary classification score and obtain the thermal stability, which is the intermediate classification score, through the formula; Obtain the maximum temperature reached by the chip and the initial temperature value when the chip starts working, and calculate the thermal runaway risk coefficient using the formula; Analyze and judge the thermal runaway risk of the chip based on the obtained thermal runaway risk coefficient; The chip combination matching degree between chips is optimized, and the objective function is constructed by NSGA-II multi-objective genetic algorithm to calculate the chip combination matching degree after iteration.

4. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method of the adaptive parameter extraction is: Using Gaussian kernel function The nonlinear features are mapped, where σ is the function bandwidth parameter and σ = 0.

5. The photoelectric parameter data is mapped from the low-dimensional space to the high-dimensional feature space, and then the key features are extracted through principal component analysis. The principal components with a cumulative contribution rate ≥ 95% are retained, the color difference of the LED lamp corresponding to the chip is obtained, and the feature weight vectors are generated, which correspond to the light efficiency level, thermal stability, and color difference respectively.

5. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method of constructing the collaborative work model is: Based on the photoelectric parameter data, a thermal-electric-optical multi-physics field collaborative working model is constructed. Build a collaborative working model; in, is the partial derivative of temperature T with respect to time t, is the Laplace operator of temperature T, α is the thermal diffusivity, c p is the material specific heat capacity, ρ is the material density, η(T) is the light efficiency level at temperature T, η0 is the initial light efficiency level at the preset room temperature, Φ total is the total luminous flux, θ i is the light output angle offset of the i-th LED lamp, i is the LED lamp index, n is the total number of LED lamps, η i (T j ) is the junction temperature T j The corresponding light effect level, P i is the power of the i-th LED lamp.

6. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method of adjusting the chip load state is: Based on the establishment of a collaborative working model, the chip load state is dynamically adjusted and a dual closed-loop control strategy is designed: Inner loop: distributes current based on LM5069 chip; Outer loop: Through the fuzzy neural network FNN controller, the input is the temperature gradient and current imbalance, and the output is pulse width modulation PWM regulation; Deploy a 5×5 thermocouple array, use Kalman filtering to reduce noise, generate a heat distribution cloud map and use the formula: The thermal uniformity index HUI is calculated.

7. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method of calculating the working stability value is: The stability state of the system is determined by defining the working stability value, obtaining the junction temperature and drive current of the LED lamp within the sampling period, calculating the standard deviation of the junction temperature and the standard deviation of the drive current within the sampling period, and obtaining the working stability value through the formula; Based on the obtained working stability value, it is determined whether the stability state of the system is stable.

8. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method of the modified chip driving strategy is: Based on the driving current and the ambient temperature and humidity, the driving current is corrected by adaptive compensation, using the correction formula: I new =I base *[1+0.02(T e -25)-0.005RH]*(1-0.0001t) Get the corrected driving current I new ; Among them, I base is the basic driving current, T e is the ambient temperature, and RH is the ambient humidity percentage.

9. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method of the spatial distribution control and energy efficiency optimization is: Adopting free-form surface secondary optical design technology, the target illumination distribution function is established to obtain the target illumination value; Based on the target illuminance value obtained, the reflective surface is optimized using non-sequential ray tracing technology. Non-sequential ray tracing simulates the propagation path of light in a complex optical system and continuously adjusts the curvature radius of the reflective surface until the actual light distribution reaches the requirement set by the target illuminance distribution function, that is, the target illuminance value.

10. The method for increasing the light flux of high-efficiency LED streetlights based on a multi-chip combination according to claim 1, characterized in that: The specific implementation method for improving the light efficiency balance is as follows: Based on the obtained light efficiency level and junction temperature, the particle swarm algorithm is used to optimize the light efficiency balance and define the light efficiency gain factor through the formula: The light efficiency gain factor K is calculated; Among them, η new is the optimized light efficiency level, η old is the initial light efficiency level, is the initial maximum junction temperature, is the maximum junction temperature after optimization; The light efficiency level is improved based on the obtained light efficiency gain factor.

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