CN112953636A - non-Lambert LED space beam model fitting scheme based on genetic algorithm - Google Patents
non-Lambert LED space beam model fitting scheme based on genetic algorithm Download PDFInfo
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Abstract
The invention relates to the technical field of visible light communication, in particular to a non-Lambert LED space beam model fitting scheme based on a genetic algorithm, which comprises the steps of obtaining actual measurement data of a non-Lambert LED beam model; constructing an initial non-Lambertian LED beam model; constructing a fitness function, and iteratively optimizing a non-Lambert LED beam model by using a genetic algorithm to obtain an optimal solution; and determining the fitted non-Lambertian LED beam model according to the optimal solution. According to the method, the initial non-Lambert LED beam model is constructed, the fitness function is constructed, the non-Lambert LED beam model is iteratively optimized by utilizing the genetic algorithm, the optimal solution is obtained, fitting of the non-Lambert LED space beam model is achieved, the space distribution characteristic and the coverage effect of visible light signals emitted by the non-Lambert LED light source are guaranteed, the calculation complexity of the fitting process is reduced based on the characteristics of the genetic algorithm, the fitting convergence speed is improved, and the method can be effectively applied to scenes such as visible light communication application scenes based on the non-Lambert LED light source.
Description
Technical Field
The invention relates to the technical field of visible light communication, in particular to a non-Lambert LED space beam model fitting scheme based on a genetic algorithm.
Background
An LED light source related to a visible light wireless technology is an illumination infrastructure, and in order to meet illumination characteristics of different scenes such as indoor scenes, outdoor scenes, roads, vehicles and tunnels, secondary light distribution elements such as a reflection cup and a free-form surface lens are generally required to be additionally arranged on an actual commercial LED light source to construct customized LED radiation characteristics facing to differentiated application scenes. In the field of lighting engineering, the radiation intensity values of the customized LEDs in different spatial directions can be obtained by performing point-by-point actual measurement in a three-dimensional space through a reference photometer, and the actual measurement values can meet the requirements of illumination distribution analysis and evaluation of an illumination coverage area. However, in the field of visible light communication based on the LED lighting infrastructure, a visible light communication system and a network planning designer must rely on an analytical expression of an LED spatial beam to construct a multipath propagation mathematical model of a visible light communication link, and finally evaluate the coverage characteristics of visible light communication and the potential gains of related system enabling technologies, thereby implementing the construction of the visible light communication system based on the LED lighting infrastructure. However, the analytical paradigm based on the lambertian LED spatial beam model is difficult or even impossible to apply to many system-level or link-level analyses based on the non-lambertian LED spatial beam model.
Disclosure of Invention
The invention provides a non-Lambert LED space beam model fitting scheme based on a genetic algorithm, overcomes the defects of the prior art, and can effectively solve the problem that the non-Lambert LED space beam model cannot be effectively fitted.
One of the technical schemes of the invention is realized by the following measures: a non-Lambertian LED spatial beam model fitting scheme based on a genetic algorithm, comprising:
obtaining actual measurement data of the non-Lambert LED beam model, wherein the actual measurement data comprises radiation intensities of different space orientations, which are actually measured in a three-dimensional space by the non-Lambert LED beam model;
constructing an initial non-Lambertian LED beam model;
constructing a fitness function, and iteratively optimizing a non-Lambert LED beam model by using a genetic algorithm to obtain an optimal solution;
and determining the fitted non-Lambertian LED beam model according to the optimal solution.
The following is further optimization or/and improvement of the technical scheme of the invention:
the initial non-Lambertian LED beam model comprises a non-Lambertian LED beam model with circularly symmetric beams and a non-Lambertian LED beam model with non-circularly symmetric beams;
the non-lambertian LED beam model with circumferentially symmetric beams is as follows:
wherein theta is a pitch angle of the emergent direction of the light beam; n is a radical of1And N2Respectively the number of cosine power functions and Gaussian power functions introduced in the initial setting; parameter A1i、A2i、A3iThe amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function are respectively; parameter B1j、B2j、B3jRespectively an amplitude coefficient, a phase deviation coefficient and a phase normalization coefficient of a jth Gaussian power function; c is an exponential decay constant within the Gaussian power function;
the non-lambertian LED beam model with non-circularly symmetric beams is as follows:
wherein theta is a pitch angle of the emergent direction of the light beam; phi is the azimuth angle of the emergent direction of the light beam; parameter A1i、A2i、A3iAre respectively the ithAmplitude coefficient, phase offset coefficient and power coefficient of cosine power function; parameter B1j、B2j、B3j、B4jThe amplitude coefficient, the phase deviation coefficient, the cosine normalization coefficient and the sine normalization coefficient of the jth Gaussian power function are respectively; c is an exponential decay constant within the gaussian power function.
The iterative optimization of the non-lambertian LED beam model by using the genetic algorithm to obtain an optimal solution includes:
constructing an initial chromosome population, wherein the initial chromosome population comprises parameters in an initial non-lambertian LED beam model;
carrying out primary screening, secondary screening and mutation on the initial chromosome population through a selection operator, a crossover operator and a mutation operator to generate a new chromosome population;
according to the new chromosome population, obtaining a fitness function value of the correspondingly fitted non-Lambert light source beam model;
and determining an iteration termination condition group by using a fitness function, judging whether to terminate iteration according to the iteration termination condition group, terminating iteration in response to meeting any iteration termination condition in the iteration termination condition group, and outputting an optimal solution.
The iteration termination condition set comprises:
the iterative search times are equal to the maximum iterative times;
the fitness function value of the optimal chromosome individual in the chromosome population exceeds a preset threshold value;
and the absolute difference degree of the fitness function values of the optimal chromosome individuals in the populations of the adjacent generations is smaller than the preset deviation setting.
The constructing the fitness function includes:
calculating the deviation of the fitted non-Lambert LED beam model between the beam intensity of each measured space direction and the measured data of the non-Lambert LED beam model, and cumulatively summing the absolute values of all the deviations;
accumulating and summing the measured data of the non-Lambert LED beam model;
calculating an absolute difference value between the absolute value accumulated summation result of each deviation and the actually measured data accumulated summation result of the non-Lambert LED beam model;
and calculating a relative ratio between the absolute difference value and the accumulated sum of the measured data of the non-Lambert LED beam model.
The second technical scheme of the invention is realized by the following measures: a non-Lambertian LED space beam model fitting device based on a genetic algorithm comprises:
the importing unit is used for obtaining actual measurement data of the non-Lambert LED beam model; actually measured data comprise the radiation intensities of different space orientations obtained by actually measuring the non-Lambert LED beam model in a three-dimensional space;
the model construction unit is used for constructing an initial non-Lambert LED beam model;
the genetic algorithm computing unit is used for constructing a fitness function and iteratively optimizing a non-Lambert LED beam model by utilizing a genetic algorithm to obtain an optimal solution;
and the determining unit is used for determining the fitted non-Lambert LED beam model according to the optimal solution.
The embodiment of the invention constructs the initial non-Lambert LED beam model, constructs the fitness function, and utilizes the genetic algorithm to iteratively optimize the non-Lambert LED beam model to obtain the optimal solution, realizes the fitting of the non-Lambert LED space beam model, ensures the space distribution characteristic and the coverage effect of the visible light signal emitted by the non-Lambert LED light source, based on the characteristics of a genetic algorithm, the calculation complexity of the fitting process is reduced, the fitting convergence speed is improved, and the method can be effectively applied to a visible light communication application scene based on a non-Lambert LED light source, a visible light positioning application scene based on the non-Lambert LED light source, a visible light sensing application scene based on the non-Lambert LED light source, a visible light distance application scene based on the non-Lambert LED light source, a mixed communication application scene based on the non-Lambert LED light source and a traditional Radio Frequency (RF) technology, a reverse communication application scene based on the non-Lambert LED light source and a Modulation Reverse Reflector (MRR), and the like.
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FIG. 1 is a flow chart of a fitting method according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of the fitting effect of the two-dimensional space according to the embodiment of the present invention.
Fig. 3 is a schematic diagram of the fitting effect of the three-dimensional space according to the embodiment of the present invention.
FIG. 4 is a flowchart illustrating the operation of the genetic algorithm according to the embodiment of the present invention.
FIG. 5 is a flowchart of a method for constructing a fitness function according to an embodiment of the present invention.
FIG. 6 is a flow chart of the calculation of the selection operator according to the embodiment of the present invention.
FIG. 7 is a flowchart illustrating the calculation of the crossover operator according to an embodiment of the present invention.
FIG. 8 is a flowchart illustrating mutation operator calculation according to an embodiment of the present invention.
FIG. 9 is a schematic diagram showing the structure of chromosome population according to the embodiment of the present invention.
Fig. 10 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
The present invention is not limited by the following examples, and specific embodiments may be determined according to the technical solutions and practical situations of the present invention.
The invention is further described with reference to the following examples and figures:
example 1: as shown in fig. 1, 2 and 3, the present embodiment discloses a non-lambertian LED spatial beam model fitting scheme based on a genetic algorithm, which includes:
step S101, obtaining actual measurement data of a non-Lambert LED beam model, wherein the actual measurement data comprises radiation intensities of different space directions obtained by actual measurement of the non-Lambert LED beam model in a three-dimensional space;
step S102, constructing an initial non-Lambert LED beam model;
s103, constructing a fitness function, and iteratively optimizing a non-Lambert LED beam model by using a genetic algorithm to obtain an optimal solution;
and step S104, determining the fitted non-Lambert LED beam model according to the optimal solution.
Through the steps, the genetic algorithm is introduced into the fitting process of the non-Lambert LED beam model, and the diversity and complexity of the non-Lambert LED beam and the practical requirements of the beam fitting process on universality, convergence, high efficiency and low computational complexity are comprehensively considered. The embodiment of the invention obtains the optimal solution by constructing the initial non-Lambert LED beam model, constructing the fitness function and solving the initial non-Lambert LED beam model by using the genetic algorithm, realizes the fitting of the non-Lambert LED space beam model, ensures the space distribution characteristic and the coverage effect of visible light signals emitted by the non-Lambert LED light source, based on the characteristics of a genetic algorithm, the calculation complexity of the fitting process is reduced, the fitting convergence speed is improved, and the method can be effectively applied to a visible light communication application scene based on a non-Lambert LED light source, a visible light positioning application scene based on the non-Lambert LED light source, a visible light sensing application scene based on the non-Lambert LED light source, a visible light distance application scene based on the non-Lambert LED light source, a mixed communication application scene based on the non-Lambert LED light source and a traditional Radio Frequency (RF) technology, a reverse communication application scene based on the non-Lambert LED light source and a Modulation Reverse Reflector (MRR), and the like.
In the step S101, the non-lambertian LED beam model actually measures the radiation intensities of different spatial orientations in the three-dimensional space, the measuring tool can use a spectrophotometer, and the radiation intensities of different spatial orientations obtained by actual measurement are presented in the form of a measurement data sheet. After the measured data of the non-Lambert LED beam model is recorded and obtained, the pitch angle theta of the emergent direction of the light beam is the azimuth angle phi of the emergent direction of the light beam, and a two-dimensional measured data matrix of two dimensions of the pitch angle theta and the azimuth angle phi is constructed.
Example 2: the embodiment discloses a non-Lambert LED space beam model fitting scheme based on a genetic algorithm, wherein the initial non-Lambert LED beam model comprises a non-Lambert LED beam model with circularly symmetric beams and a non-Lambert LED beam model with non-circularly symmetric beams;
the non-lambertian LED beam model with circumferentially symmetric beams is as follows:
wherein θ isThe pitch angle of the emergent direction of the light beam; n is a radical of1And N2Respectively the number of cosine power functions and Gaussian power functions introduced in the initial setting; parameter A1i、A2i、A3iAmplitude coefficient, phase offset coefficient and power coefficient (i.e. power exponent coefficient) of the ith cosine power function, respectively, and in order not to lose generality, A1iIs in the value range of [0,1 ]]，A2iIs in the range of [0,90 DEG ]]，A3iIs in the range of [0,100 ]](ii) a Parameter B1j、B2j、B3jRespectively the amplitude coefficient, the phase deviation coefficient and the phase normalization coefficient of the jth Gaussian power function, and B1jIs in the value range of [0,1 ]]，B2jIs in the range of [0,90 DEG ]]，B3jIs in the range of [0,90 DEG ]](ii) a C is an exponential decay constant within the gaussian power function for adjusting the decay rate of the gaussian power function.
The non-lambertian LED beam model with non-circularly symmetric beams is as follows:
wherein theta is a pitch angle of the emergent direction of the light beam; phi is the azimuth angle of the emergent direction of the light beam; parameter A1i、A2i、A3iAmplitude coefficient, phase offset coefficient and power coefficient (i.e. power exponent coefficient) of the ith cosine power function, respectively, and in order not to lose generality, A1iIs in the value range of [0,1 ]]，A2iIs in the range of [0,90 DEG ]]，A3iIs in the range of [0,100 ]](ii) a Parameter B1j、B2j、B3j、B4jRespectively the amplitude coefficient, the phase offset coefficient, the cosine normalized coefficient and the sine normalized coefficient of the jth Gaussian power function, and B1jIs in the value range of [0,1 ]]，B2jIs in the range of [0,90 DEG ]]，B3jIs in the value range of [0,1 ]]，B4jIs in the value range of [0,1 ]](ii) a C is an exponential decay constant within the Gaussian power function for adjusting the decay of the Gaussian power functionThe rate of deceleration.
Example 3: as shown in fig. 4, the embodiment discloses a non-lambertian LED spatial beam model fitting scheme based on a genetic algorithm, where the constructing a fitness function and iteratively optimizing the non-lambertian LED beam model by using the genetic algorithm to obtain an optimal solution includes:
step S201, constructing an initial chromosome population, wherein the initial chromosome population comprises parameters in an initial non-Lambertian LED beam model;
step S202, carrying out primary screening, secondary screening and mutation on the initial chromosome population through a selection operator, a crossover operator and a mutation operator to generate a new chromosome population;
step S203, constructing a fitness function; according to the new chromosome population, obtaining a fitness function of the correspondingly fitted non-Lambert light source beam model;
and S204, determining an iteration termination condition group by using a fitness function, judging whether to terminate iteration according to the iteration termination condition group, terminating iteration in response to meeting any iteration termination condition in the iteration termination condition group, and outputting an optimal solution.
Since the fitness function is used for reflecting the matching degree of the fitted non-lambertian LED beam model and the non-lambertian LED light source space beam characteristics obtained through actual measurement, the higher the matching degree is, the farther the corresponding fitness function value is from 0% and approaches to 100%, and the worse the matching degree between the two is, the closer the value of the corresponding fitness function is from 0% and deviates from 100%. Therefore, the constructing of the fitness function in step S203 is shown in fig. 5, and includes:
step S2031, calculating the deviation between the beam intensity of the fitted non-Lambert LED beam model in each measured space direction and the measured data of the corresponding non-Lambert LED beam model, and cumulatively summing the absolute values of the deviations; the measured spatial direction is the spatial direction measured in the measured data of the non-Lambert LED beam model;
step S2032, accumulating and summing the measured data of the non-Lambert LED beam model; the radiation intensities of different space orientations obtained by actually measuring a non-Lambert LED beam model in a three-dimensional space are accumulated and summed;
step S2033, calculating the absolute difference value between the accumulated summation result of the absolute values of all the deviations and the accumulated summation result of the actually measured data of the non-Lambert LED beam model;
step S2034, calculating the relative ratio between the absolute difference and the accumulated summation of the actually measured data of the non-Lambert LED beam model.
In step S201, an initial chromosome population is constructed, where chromosome individuals are one-dimensional vectors, and chromosome individuals in the initial chromosome population corresponding to the initial non-lambertian LED beam (circular symmetry) model are chromosome individuals
Chromosome individuals in the initial chromosome population corresponding to the non-Lambert LED beam (non-circular symmetric) model are
The structural defect of the chromosome individual is that three adjustable parameters of the same basic function are adjacently arranged together, so that in order to avoid the defects, the chromosome individual with the three adjustable parameters of different basic functions arranged in a staggered way is introduced, and the coefficients at the same positions of different basic functions are intensively arranged together:
the initial non-Lambertian LED beam model corresponds to an initial chromosome population of
Wherein SkRepresents NPopulationChromosome population of chromosome individual the kth chromosome individualI.e. SkCan be expressed as
Same as above
Can be expressed as
The number of chromosome individuals in the chromosome population is NPopulationAnd (4) respectively.
In step S202, the initial chromosome population is subjected to primary screening, secondary screening and mutation by the selection operator, the crossover operator and the mutation operator, so as to generate a new chromosome population, which specifically includes the following steps:
the selection operator includes a random uniform operator and a competition selection operator as shown in fig. 6, and the screening is performed once. The random uniform operator operation is that in an initial chromosome population, the selection probability (individual fitness/total individual accumulated fitness) of each chromosome individual is firstly calculated, and then chromosomes of N chromosome individuals are selected at one time at equal intervals according to the selection probability of the chromosome individuals to generate a high-fitness chromosome population; the competition selection operator randomly selects two or three chromosomes in the initial chromosome population, compares the fitness of chromosome individuals to select the optimal chromosome, and repeats the selection process until N chromosome individuals are selected to generate the high-fitness chromosome population.
The crossover operator includes a single-point crossover operator and a double-point crossover operator as shown in fig. 7, and secondary screening is performed. The operation process comprises the steps of firstly selecting two chromosome individuals from a chromosome group of a current species, randomly generating a random number, carrying out cross operation when the random number is smaller than the cross probability, and not carrying out the cross operation if the random number is not smaller than the cross probability. The single-point crossing operator operation is to select a crossing point at all gene positions of the chromosome individual, and the front part and the rear part of the gene of the crossing point of the two chromosomes are crossed and interchanged to generate two new chromosomes. The double-point crossover operator selects two crossover points at all gene positions of chromosome individuals, and the front parts and the rear parts of the genes of the two crossover points of the two chromosomes are respectively crossed and interchanged to generate new two chromosomes.
As shown in fig. 8, the mutation operator performs mutation, and the operation process includes firstly setting mutation probability, then generating a random number for each gene of each chromosome, when the random number is smaller than the mutation probability, the gene is mutated to generate a value conforming to the value range of the position to replace the original value, and if the random number is not smaller than the mutation probability, no mutation operation occurs, and the value of the position of the gene is retained.
In conclusion, in the process of calculating by using the genetic algorithm, the construction process of the chromosome population is as shown in fig. 9, the initial population chromosomes are constructed by an initial model and random model parameters, the initial model is divided into a symmetrical and asymmetrical non-lambertian LED beam model, and the model parameters take values randomly in the parameter value range; the high fitness chromosome population is a chromosome population which is selected by a selection operator and accords with the number of chromosomes of the measuring beam, and is selected as a chromosome individual with higher fitness; the new chromosome population is generated by the cross exchange of chromosome individuals; and then, performing mutation on each gene position of each chromosome through mutation operator operation to generate a brand-new chromosome population.
In step S205, it is determined whether to terminate the iteration according to the iteration termination condition group, and if any iteration termination condition in the iteration termination condition group is satisfied, the iteration is terminated, and an optimal solution is output.
The set of iteration termination conditions comprises:
1. the iterative search times are equal to the maximum iterative times;
2. the fitness function value of the optimal chromosome individual in the chromosome population exceeds a preset threshold value;
3. and the absolute difference degree of the fitness function values of the optimal chromosome individuals in the populations of the adjacent generations is smaller than the preset deviation setting.
During judgment, firstly judging the iteration termination condition 1, outputting an optimal solution in response to the satisfaction, judging the iteration termination condition 2 in response to the non-satisfaction, outputting an optimal solution in response to the satisfaction, judging the iteration termination condition 3 in response to the non-satisfaction, outputting an optimal solution in response to the satisfaction, and continuing an iteration process of performing primary screening, secondary screening and mutation through a selection operator, a crossover operator and a mutation operator and judging through the iteration termination condition group by using a new chromosome population generated after the calculation of the mutation operator in response to the non-satisfaction.
Example 4: as shown in fig. 10, the present embodiment discloses a non-lambertian LED spatial beam model fitting apparatus based on genetic algorithm, which includes:
the importing unit is used for obtaining actual measurement data of the non-Lambert LED beam model, and the actual measurement data comprises the radiation intensities of different space orientations, which are actually obtained by the non-Lambert LED beam model in a three-dimensional space;
the model construction unit is used for constructing an initial non-Lambert LED beam model;
the genetic algorithm computing unit is used for constructing a fitness function and iteratively optimizing a non-Lambert LED beam model by utilizing a genetic algorithm to obtain an optimal solution;
and the determining unit is used for determining the fitted non-Lambert LED beam model according to the optimal solution.
Embodiment 5, a storage medium having stored thereon a computer program readable by a computer, the computer program being arranged to, when run, perform a non-lambertian LED spatial beam model fitting method based on a genetic algorithm.
The storage medium may include, but is not limited to: u disk, read-only memory, removable hard disk, magnetic or optical disk, etc. various media capable of storing computer programs.
Embodiment 6, the electronic device, comprising a processor and a memory, said memory having stored thereon a computer program that is loaded and executed by the processor to implement a non-lambertian LED spatial beam model fitting method based on a genetic algorithm. The electronic equipment further comprises transmission equipment and input and output equipment, wherein the transmission equipment and the input and output equipment are both connected with the processor.
The above technical features constitute the best embodiment of the present invention, which has strong adaptability and best implementation effect, and unnecessary technical features can be increased or decreased according to actual needs to meet the requirements of different situations.
Claims (9)
1. A non-Lambertian LED space beam model fitting scheme based on a genetic algorithm is characterized by comprising the following steps:
obtaining actual measurement data of the non-Lambert LED beam model, wherein the actual measurement data comprises radiation intensities of different space orientations, which are actually measured in a three-dimensional space by the non-Lambert LED beam model;
constructing an initial non-Lambertian LED beam model;
constructing a fitness function, and iteratively optimizing a non-Lambert LED beam model by using a genetic algorithm to obtain an optimal solution;
and determining the fitted non-Lambertian LED beam model according to the optimal solution.
2. The genetic algorithm-based non-lambertian LED spatial beam model fitting scheme of claim 1 wherein the initial non-lambertian LED beam model comprises a non-lambertian LED beam model with a circumferentially symmetric beam and a non-lambertian LED beam model with a non-circumferentially symmetric beam;
the non-lambertian LED beam model with circumferentially symmetric beams is as follows:
wherein theta is a pitch angle of the emergent direction of the light beam; n is a radical of1And N2Of cosine and gaussian power functions introduced in the initial setting, respectivelyThe number of the cells; parameter A1i、A2i、A3iThe amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function are respectively; parameter B1j、B2j、B3jRespectively an amplitude coefficient, a phase deviation coefficient and a phase normalization coefficient of a jth Gaussian power function; c is an exponential decay constant within the Gaussian power function;
the non-lambertian LED beam model with non-circularly symmetric beams is as follows:
wherein theta is a pitch angle of the emergent direction of the light beam; phi is the azimuth angle of the emergent direction of the light beam; parameter A1i、A2i、A3iThe amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function are respectively; parameter B1j、B2j、B3j、B4jThe amplitude coefficient, the phase deviation coefficient, the cosine normalization coefficient and the sine normalization coefficient of the jth Gaussian power function are respectively; c is an exponential decay constant within the gaussian power function.
3. The genetic algorithm-based non-lambertian LED spatial beam model fitting scheme of claim 1 or 2, wherein the iterative optimization of the non-lambertian LED beam model using the genetic algorithm to obtain an optimal solution comprises:
constructing an initial chromosome population, wherein the initial chromosome population comprises parameters in an initial non-lambertian LED beam model;
carrying out primary screening, secondary screening and mutation on the initial chromosome population through a selection operator, a crossover operator and a mutation operator to generate a new chromosome population;
according to the new chromosome population, obtaining a fitness function value of the correspondingly fitted non-Lambert light source beam model;
and determining an iteration termination condition group by using a fitness function, judging whether to terminate iteration according to the iteration termination condition group, terminating iteration in response to meeting any iteration termination condition in the iteration termination condition group, and outputting an optimal solution.
4. The genetic algorithm-based non-lambertian LED spatial beam model fitting scheme of claim 3 wherein the set of iteration termination conditions comprises:
the iterative search times are equal to the maximum iterative times;
the fitness function value of the optimal chromosome individual in the chromosome population exceeds a preset threshold value;
and the absolute difference degree of the fitness function values of the optimal chromosome individuals in the populations of the adjacent generations is smaller than the preset deviation setting.
5. The genetic algorithm-based non-Lambertian LED spatial beam model fitting scheme according to claim 1, 2 or 4, wherein the constructing a fitness function comprises:
calculating the deviation of the fitted non-Lambert LED beam model between the beam intensity of each measured space direction and the measured data of the non-Lambert LED beam model, and cumulatively summing the absolute values of all the deviations;
accumulating and summing the measured data of the non-Lambert LED beam model;
calculating an absolute difference value between the absolute value accumulated summation result of each deviation and the actually measured data accumulated summation result of the non-Lambert LED beam model;
and calculating a relative ratio between the absolute difference value and the accumulated sum of the measured data of the non-Lambert LED beam model.
6. The genetic algorithm-based non-Lambertian LED spatial beam model fitting scheme of claim 3, wherein the constructing a fitness function comprises:
calculating the deviation of the fitted non-Lambert LED beam model between the beam intensity of each measured space direction and the measured data of the non-Lambert LED beam model, and cumulatively summing the absolute values of all the deviations;
accumulating and summing the measured data of the non-Lambert LED beam model;
calculating an absolute difference value between the absolute value accumulated summation result of each deviation and the actually measured data accumulated summation result of the non-Lambert LED beam model;
and calculating a relative ratio between the absolute difference value and the accumulated sum of the measured data of the non-Lambert LED beam model.
7. A non-Lambertian LED space beam model fitting device based on a genetic algorithm is characterized by comprising:
the importing unit is used for obtaining actual measurement data of the non-Lambert LED beam model; actually measured data comprise the radiation intensities of different space orientations obtained by actually measuring the non-Lambert LED beam model in a three-dimensional space;
the model construction unit is used for constructing an initial non-Lambert LED beam model;
the genetic algorithm computing unit is used for constructing a fitness function and iteratively optimizing a non-Lambert LED beam model by utilizing a genetic algorithm to obtain an optimal solution;
and the determining unit is used for determining the fitted non-Lambert LED beam model according to the optimal solution.
8. A storage medium having stored thereon a computer program readable by a computer, the computer program being arranged to perform the genetic algorithm based non-lambertian LED spatial beam model fitting scheme of any of claims 1 to 5 when executed.
9. An electronic device comprising a processor and a memory, the memory having stored thereon a computer program that is loaded and executed by the processor to implement the genetic algorithm based non-lambertian LED spatial beam model fitting scheme of any of claims 1 to 5.
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