CN112949810A - Particle swarm optimization combined beam fitting method for improving visible light wireless technology - Google Patents
Particle swarm optimization combined beam fitting method for improving visible light wireless technology Download PDFInfo
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Abstract
The invention relates to the technical field of visible light wireless communication, in particular to a method for improving particle swarm optimization combined beam fitting in a visible light wireless technology, which comprises the following steps: acquiring actual measurement data of a non-Lambert commercial solid-state light source; constructing an initial non-Lambertian commercial solid-state light source beam model; obtaining a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, optimizing the particle swarm by using a particle swarm optimization algorithm, and outputting an optimal solution; a fitted non-lambertian commercial solid state light source beam model is determined. The particle swarm optimization algorithm is introduced into the practical design of the optimization fitting of the commercial light source combined beam of the visible light wireless technology, the particle swarm optimization algorithm is utilized to perform heuristic search optimization on each correlation coefficient in a plurality of basic functions of the initial non-Lambert commercial solid-state light source beam model, the advantage of strong global optimality of the search result of the particle swarm optimization algorithm is fully exerted, and the problem that other optimization algorithms are too early converged to cause the limitation of local optimality is avoided.
Description
Technical Field
The invention relates to the technical field of visible light wireless communication, in particular to a particle swarm optimization combined beam fitting method for improving a visible light wireless technology.
Background
As the development of visible light wireless technology continues to be deep, technicians often need to face a combined beam configuration that is far more complex than a lambertian beam when designing and analyzing visible light wireless systems based on commercial light sources. The solid-state light source beam customization characteristics are outstanding, the space characteristics are various, the modeling complexity is high, and different beam light sources can be combined to form a solid-state light source array with mixed beams.
When the solid-state light source array is customized, lighting fixture manufacturers usually need to design secondary light distribution by means of a beam pattern. The typical secondary light distribution design method comprises the step of additionally arranging a secondary light distribution element such as a reflecting cup, a free-form surface lens and the like on an initial solid-state light source. Generally, the solid-state light source subjected to secondary light distribution by a manufacturer can project most of light power to a preset illumination area, and the specific illumination area geometry can be circular, elliptical or even approximately rectangular. To ensure the uniformity of the illumination coverage, it is generally necessary that the illumination levels at different locations within the illumination area are substantially similar; on the other hand, areas outside the predetermined illumination area, especially adjacent areas, require a drastic reduction in illumination level to avoid wasted illumination coverage and potential light pollution. In order to meet the above-mentioned illumination engineering objectives, the solid-state light source with the customized secondary light distribution usually has a complex combined light beam pattern characteristic, which is hard to be compared with the traditional lambertian beam pattern. Therefore, the basic requirements of the visible light wireless technology are met, and the optimal fitting of the commercial solid-state light source combined beam is completed, but no effective method is available at present for completing the fitting of the non-Lambert commercial solid-state light source.
Disclosure of Invention
The invention provides a particle swarm optimization combined beam fitting method for improving a visible light wireless technology, overcomes the defects of the prior art, and can effectively solve the problem that a non-Lambert commercial solid-state light source cannot be effectively fitted.
One of the technical schemes of the invention is realized by the following measures: a method for improving particle swarm optimization combined beam fitting for a visible light wireless technology comprises the following steps:
obtaining actual measurement data of the non-Lambert commercial solid-state light source, wherein the actual measurement data comprises actual measurement values generated by point-by-point testing of the radiation intensity of the non-Lambert commercial solid-state light source in different space directions in a three-dimensional space;
constructing an initial non-Lambertian commercial solid-state light source beam model;
obtaining a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, optimizing the particle swarm by using a particle swarm optimization algorithm, and outputting an optimal solution, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of the obtained beam by using actual measurement data of the non-Lambert commercial solid-state light source;
and determining the fitted non-Lambertian commercial solid-state light source 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 commercial solid-state light source beam model described above includes:
the initial non-lambertian commercial solid-state light source beam model, if its beam is circularly symmetric, 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; a. the1i、A2i、A3iThe amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function are respectively; b is1j、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 initial non-lambertian commercial solid-state light source beam model, if its beam is non-circularly symmetric and the beam edge is smooth, is as follows:
wherein theta is the pitch angle of the emergent direction of the light beam, and phi is the azimuth 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; a. the1i、A2i、A3iThe amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function are respectively; b is1j、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 initial non-lambertian commercial solid-state light source beam model, if its beam is non-circularly symmetric and the beam edge is sharp, is as follows:
wherein theta is the pitch angle of the emergent direction of the light beam, and phi is the azimuth angle of the emergent direction of the light beam; gi(theta) is Gi(θ)＝gi1-gi2exp[-gi3(|θ|-gi4)2](ii) a Step function UiIs composed of
Coefficient function gi5(phi) is
Peak angle function thetaip(phi) is
The above-mentioned particle swarm is obtained according to the initial commercial solid-state light source beam model of non-lambertian, utilizes the particle swarm optimization algorithm to optimize the particle swarm, outputs the optimal solution, includes:
constructing a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of beams obtained by utilizing actual measurement data of the non-Lambert commercial solid-state light source;
constructing a weight factor, a learning factor and a cost function;
performing heuristic search on the particle swarm according to the weight factor, the learning factor and the cost function;
and setting a search termination condition group, judging whether to terminate the search by using the search termination condition group, and terminating the search and outputting an optimal solution in response to meeting any search termination condition in the search termination condition group.
The search termination condition set includes:
the searching times of the particle swarm are equal to the preset maximum iteration times;
searching to obtain a cost function of the optimal particle individuals in the particle swarm, wherein the cost function is larger than a preset threshold value;
the absolute difference degree of the cost function of the optimal particle individuals in the adjacent generations of particle swarms is smaller than the preset deviation setting.
The cost function includes:
calculating deviation between the beam intensity of the fitted non-Lambert commercial solid-state light source beam model in each measured space direction and the corresponding beam intensity in the measured data, and cumulatively summing absolute values of the deviations;
accumulating and summing the intensity of each wave beam in the measured data;
calculating an absolute difference value between the accumulated sum result of the absolute values of the deviations and the accumulated sum result of the measured data;
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 device for improving particle swarm optimization combined beam fitting for visible light wireless technology comprises:
the importing unit is used for obtaining actual measurement data of the non-Lambert commercial solid-state light source, wherein the actual measurement data comprises actual measurement values generated by testing the radiation intensity of the non-Lambert commercial solid-state light source in different space directions point by point in a three-dimensional space;
the model building unit is used for building an initial non-Lambert commercial solid-state light source beam model;
the particle swarm optimization calculation unit is used for obtaining a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, optimizing the particle swarm by using a particle swarm optimization algorithm and outputting an optimal solution, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of the obtained beam by using actual measurement data of the non-Lambert commercial solid-state light source;
and the determining unit is used for determining the fitted non-Lambert commercial solid-state light source beam model according to the optimal solution.
The particle swarm optimization algorithm is introduced into the practical design of the optimization fitting of the commercial light source combined beam of the visible light wireless technology, the particle swarm optimization algorithm is utilized to perform heuristic search optimization on each correlation coefficient in a plurality of basic functions of the initial non-Lambert commercial solid-state light source beam model, the advantage of strong global optimality of the search result of the particle swarm optimization algorithm is fully exerted, and the problem that other optimization algorithms are too early converged to cause the limitation of local optimality is avoided. According to the embodiment of the invention, an initial non-Lambert commercial solid-state light source beam model is constructed, the particle swarm is obtained according to the initial non-Lambert commercial solid-state light source beam model, the particle swarm is optimized by utilizing a particle swarm optimization algorithm, an optimal solution is output, the fitting of the non-Lambert commercial solid-state light source beam model is realized, the whole fitting process has no adjustment of many parameters, and a speed block approaching the optimal solution is approached, so that the system parameters can be effectively optimized, and the optimization process of a correlation function is satisfied. Meanwhile, the method can be applied to various application scenes such as a visible light communication application scene based on the non-Lambert commercial solid-state light source, a visible light positioning application scene based on the non-Lambert commercial solid-state light source and the like.
Drawings
FIG. 1 is a flow chart of a method of 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 of a particle swarm optimization method according to an embodiment of the present invention.
FIG. 5 is a flow chart of a method for constructing a population of particles in an embodiment of the present invention.
FIG. 6 is a flow chart of a heuristic search method in an embodiment of the invention.
Fig. 7 is a flowchart of a method for determining a search termination condition set according to an embodiment of the present invention.
Fig. 8 is a schematic structural diagram of 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 method for improving particle swarm optimization combined beam fitting for visible light wireless technology, which includes:
step S101, obtaining actual measurement data of the non-Lambert commercial solid-state light source, wherein the actual measurement data comprises actual measurement values generated by point-by-point testing of the radiation intensity of the non-Lambert commercial solid-state light source in different space directions in a three-dimensional space;
step S102, constructing an initial non-Lambert commercial solid-state light source beam model;
step S103, obtaining a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, optimizing the particle swarm by utilizing a particle swarm optimization algorithm, and outputting an optimal solution, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of the obtained beam by utilizing actual measurement data of the non-Lambert commercial solid-state light source;
and step S104, determining the fitted non-Lambert commercial solid-state light source beam model according to the optimal solution.
Through the steps, the particle swarm optimization algorithm is introduced into the practical design of the optimization fitting of the commercial light source combined beam of the visible light wireless technology, the particle swarm optimization algorithm is utilized to perform heuristic search optimization on each correlation coefficient in a plurality of basic functions of the initial non-Lambert commercial solid-state light source beam model, the advantage of strong global optimality of the search result of the particle swarm optimization algorithm is fully exerted, and the problem that other optimization algorithms are too early converged to cause the limitation of local optimality is avoided. According to the embodiment of the invention, an initial non-Lambert commercial solid-state light source beam model is constructed, the particle swarm is obtained according to the initial non-Lambert commercial solid-state light source beam model, the particle swarm is optimized by utilizing a particle swarm optimization algorithm, an optimal solution is output, the fitting of the non-Lambert commercial solid-state light source beam model is realized, the whole fitting process has no adjustment of many parameters, the speed block of the optimal solution is approached, and the optimization process of a correlation function can be satisfied. Meanwhile, the method can be applied to various application scenes such as a visible light communication application scene based on a non-Lambert commercial solid-state light source, a visible light positioning application scene based on a non-Lambert commercial solid-state light source, a visible light sensing application scene based on a non-Lambert commercial solid-state light source, a visible light distance application scene based on a non-Lambert commercial solid-state light source, a mixed communication application scene based on a non-Lambert commercial solid-state light source and a traditional Radio Frequency (RF) technology and the like.
In step S101, the measured data of the non-lambertian commercial solid-state light source is obtained, that is, the radiation intensities of the non-lambertian commercial solid-state light source in different spatial orientations are tested point by point in the three-dimensional space, and the actual measured values are generated, and the measurement tool thereof may use a spectrophotometer, and the radiation intensities in different spatial orientations obtained by actual measurement are presented in the form of a measurement data sheet (typically in excel format). After the measured data of the non-Lambert commercial solid-state light source is recorded, 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: as shown in fig. 1, the present embodiment discloses a method for improving particle swarm optimization combined beam fitting for visible light wireless technology, wherein the initial non-lambertian commercial solid-state light source beam model further includes:
the initial non-lambertian commercial solid-state light source beam model, if its beam is circularly symmetric, 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; a. the1i、A2i、A3iRespectively the amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function, A is a constant1iCan be in the range of [0,1 ]]，A2iCan be in the range of [0,90 DEG ]]，A3iCan be in the range of [0,100 ]]；B1j、B2j、B3jRespectively the amplitude coefficient, the phase deviation coefficient and the phase normalization coefficient of the jth Gaussian power function, and B1jCan be in the range of [0,1 ]]，B2jCan be in the range of [0,90 DEG ]]，B3jCan be 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 initial non-lambertian commercial solid-state light source beam model, if its beam is non-circularly symmetric and the beam edge is smooth, is as follows:
wherein theta is the pitch angle of the emergent direction of the light beam, and phi is the azimuth angle of the emergent direction of the light beam; n is a radical of1And N2Are respectively at the initial settingDetermining the number of cosine power functions and Gaussian power functions introduced; a. the1i、A2i、A3iRespectively the amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function, A is a constant1iCan be in the range of [0,1 ]]，A2iCan be in the range of [0,90 DEG ]]，A3iCan be in the range of [0,100 ]]；B1j、B2j、B3j、B4jThe amplitude coefficient, the phase offset coefficient, the cosine normalized coefficient and the sine normalized coefficient of the jth Gaussian power function respectively have no loss of generality, and the value range of B1j can be [0,1]The value range of B2j can be [0,90 ° ]]，B3jAnd B4jCan be respectively [0,1 ]](ii) a C is an exponential decay constant within the gaussian power function for adjusting the decay rate of the gaussian power function.
The initial non-lambertian commercial solid-state light source beam model, if its beam is non-circularly symmetric and the beam edge is sharp, is as follows:
wherein theta is the pitch angle of the emergent direction of the light beam, and phi is the azimuth angle of the emergent direction of the light beam; gi(theta) is Gi(θ)＝gi1-gi2exp[-gi3(|θ|-gi4)2](ii) a Step function UiIs composed of
Coefficient function gi5(phi) is
Peak angle function thetaip(phi) is
Where the adjustable parameters include gi1、gi2、gi3、gi4、gi5x、gi5y、θipx、θipy、mi. Wherein, gi1、gi2、gi3、gi4、gi5x、gi5y、miCan be in the range of [0,100 ]]；θipx、θipyCan be respectively [0,90 DEG ]]。
Example 3: as shown in fig. 4, this embodiment discloses a method for improving particle swarm optimization combined beam fitting for a visible light wireless technology, where an initial particle swarm is obtained according to an initial non-lambertian commercial solid-state light source beam model, the initial particle swarm is optimized by using a particle swarm optimization algorithm, and an optimal solution is output, further including:
step S201, constructing a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of beams obtained by utilizing actual measurement data of the non-Lambert commercial solid-state light source;
step S202, constructing a weight factor, a learning factor and a cost function;
step S203, performing heuristic search on the initial particle swarm according to the weight factor, the learning factor and the cost function;
step S204, setting a search termination condition group, judging whether to terminate the search by using the search termination condition group, terminating the search in response to meeting any search termination condition in the search termination condition group, and outputting an optimal solution.
In the above step S201, as shown in fig. 5, the particle group includes a plurality of particles, each particle configuration process includes obtaining correlation coefficients of a plurality of functions of a beam by using measured data of a non-lambertian commercial solid-state light source, and configuring a one-dimensional vector X with the correlation coefficients, the one-dimensional vector X configuring an individual particle; the specific form is as follows:
for an initial non-lambertian commercial solid-state light source beam (circularly symmetric) model, the corresponding one-dimensional position vector X is specifically: x ═ A11 A21 A31 A12 A22 A32 …A1N1 A2 N1A3 N1 B11B21 B31B12B22B32 …B1 N2B2 N2 B3 N2]；
For an initial non-lambertian commercial solid-state light source beam (non-circularly symmetric, smooth beam edge) model, the corresponding one-dimensional position vector X is specifically: x ═ A11 A21 A31 A12 A22 A32 …A1N1 A2 N1A3 N1 B11B21 B31B41B12B22B32 B42 …B1 N2B2 N2B3 N2B4 N2]。
For an initial non-lambertian commercial solid-state light source beam (non-circular symmetric, sharp beam edge) model, the corresponding one-dimensional position vector X is specifically:
in the step S202, a weight factor, a learning factor and a cost function are constructed, which are specifically as follows:
the weighting factor W (namely the inertia weighting factor) can directly influence the capability distribution between the global search and the local search of the particles, and objectively shows the influence of the previous velocity vector of the particles on the velocity vector of the new generation. Generally, when the value of the weight factor W is large, the global search capability of the algorithm is strong, which is helpful for improving the convergence speed of the optimization search process, otherwise, the local search capability is strong, which is helpful for improving the convergence precision of the optimization search process.
In this embodiment, two types of candidate weighting factors are introduced, namely a static weighting factor and a dynamic weighting factor. Wherein the static weighting factor W ═ WSTATICIn the optimization search process, the weighting factor is always set to be a fixed value, so that the complexity of the weighting factor structure is the lowest, and the static weighting factor W in the embodiment is not generalSTATICCan be in the range of [0.7,1.4 ]]Typical values may be: 0.7, 0.9, 1.2, etc.; dynamic weighting factor W ═ WDYNAMICThe weighting factor is set to have a larger value in the early stage of the optimization search process so as to complete better global search performance, and the value of the weighting factor is gradually reduced along with the increase of the number of times of the particle swarm optimization iteration, so that in the later stage of the optimization search process, better local search performance can be realized by the smaller weighting factor, and thus, the dynamic weighting factor W in the embodiment has no loss of generalityDYNAMICThe value expression of (a) may be:
wDYNAMIC＝wMIN+(wMAX+wMIN)S(n-1)
wherein, wMINIs the minimum weight value, wMAXIs the maximum weight value, n is the current iteration number, S is the weight scaling factor, and in this embodiment, the dynamic weight factor W is used for the purpose of keeping the generality constantDYNAMICThe candidate values of the minimum weight values can be 0.5, 0.7 and 0.9; the candidate values of the maximum weight value can be 1.0, 1.2 and 1.4; the candidate values of the weight scaling factor may be 0.8, 0.85, 0.9, 0.95.
(II) learning factor (i.e. acceleration factor or cognition factor), the learning factor includes C1And C2Wherein, C1For regulating the flight of individual particles in an individual optimum direction, C2For adjusting the flight of single particles to the optimal direction of the group.
In this embodiment, two types of candidate learning factors are introduced, which are a static learning factor and a dynamic learning factor. Wherein the static learning factor C1＝C1STATIC，C2＝C2STATICIn the optimization search process, the learning factor is always set to be a fixed value, so that the complexity of the learning factor structure is the lowest, and the static learning factor C in the embodiment is not general1STATICAnd C2STATICCan be in the range of [1.2, 2.2 ]]Typical values can be 1.5, 1.75, 2.0, etc.; dynamic weight factor C1＝C1DYNAMIC，C2＝C2DYNAMICThe design idea of the weight factors is to increase the diversity of the particle swarm as much as possible to avoid over-populationTo converge to the local optimal solution early, the numeric expressions of the dynamic weighting factors C1 and C2 in this embodiment may be:
wherein, c1MAXAnd c2MAXMaximum learning factors of C1 and C2, respectively; c. C1MINAnd c2MINThe minimum learning factors of C1 and C2, respectively, N is the current iteration number, NiterFor maximum number of iterations, and for no loss of generality, c in this embodiment1MAXAnd c2MAXTypical values can be 2.0, 2.2, 2.4, etc.; c. C1MINAnd c2MINTypical values can be 0.15, 0.2, 0.25, etc., and the maximum number of iterations NiterTypical values may be 50, 60, 70, etc.
(III) the cost function should reflect the matching degree of the fitted non-Lambert commercial solid-state light source beam model and the non-Lambert commercial solid-state light source beam characteristics obtained by actual measurement, that is, the higher the matching degree between the two is, the farther the value of the corresponding cost function is from 0% and approaches to 100%, and the worse the matching degree between the two is, the closer the value of the corresponding cost function is to 0% and deviates from 100%, thereby constructing the cost function, which specifically comprises:
1. calculating deviation between the beam intensity of the fitted non-Lambert commercial solid-state light source beam model in each measured space direction and the corresponding beam intensity in the measured data, and cumulatively summing absolute values of the deviations;
2. accumulating and summing the intensity of each wave beam in the measured data;
3. calculating an absolute difference value between the accumulated sum result of the absolute values of the deviations and the accumulated sum result of the measured data;
4. a relative ratio between the absolute difference and the cumulative sum of measured data for the non-lambertian commercial solid state light source beam model is calculated.
In step S203, a heuristic search is performed on the particle swarm according to the weight factor, the learning factor, and the cost function, where the heuristic search is to update the historical optimum value of each particle and the historical optimum particle of the whole particle swarm, and complete the update of the whole particle swarm.
The heuristic search process is shown in fig. 6 and includes:
step S2031, obtaining the particle swarm of the (n-1) th search iteration, and obtaining the weight factor W and the learning factor C1And C2Then, the speed V of the m position in the Kth particle of the nth search iteration is outputk,m(n+1)。
Wherein Vk,m(n+1)＝wVk,m(n)+r1c1(pk,m(n)-Xk,m(n))+r2c2(gm(n)-Xk,m(n)), extending to the general case, the velocity of the M positions in the kth particle in the population can be obtained by:
wherein K is the serial number of the particle in the population, the value range of K is more than or equal to 1 and less than or equal to K, K is the scale of the particle swarm, namely the number of the particles contained in the particle swarm, M is the position number of the one-dimensional position vector of the particle, the value range of M is more than or equal to 1 and less than or equal to M, and M is the length of the one-dimensional position vector of the particle, namely the number of the main adjustable parameters of the initial non-Lambert commercial solid-state light source beam model; vk,M(n) is the velocity of the mth position in the kth particle after the last iteration; r is1And r2Two random shrinkage coefficients are provided, and the value range is (0, 1);
the optimal value of the Mth position history in the kth particle after the last iteration is obtained; xk,M(n) is the value of the Mth position in the kth particle after the last iteration;
taking a value of the historical best particle in the particle swarm after the last iteration at the Mth position;
step S2032, updating the m position in the Kth particle to obtain the m position X in the Kth particlek,m(n+1)。
Wherein, Xk,m(n+1)＝Xk,m(n)+Vk,m(n +1), extending to the general case, the update of the M positions in the kth particle in the population can be obtained by:
wherein n represents the nth heuristic optimization iteration, Vk,M(n +1) represents the velocity of the Mth position in the kth particle in this iteration.
Step S2033, outputting the kth particle of the nth search iteration according to step 2, as follows:
Xk,1(n+1)Xk,2(n+1)Xk,3(n+1)Xk,4(n+1)Xk,5(n+1)...Xk,M(n+1)。
step S2034, outputting the nth iteration particle swarm as follows:
X1(n+1)X2(n+1)X3(n+1)X5(n+1)X6(n+1)...XK(n+1)。
step S204, setting a search termination condition group, judging whether to terminate the search by using the search termination condition group, terminating the search in response to meeting any search termination condition in the search termination condition group, outputting an optimal solution (namely the particle swarm generated by the search iteration), and performing the search iteration again by using the particle swarm updated by the search iteration in response to not meeting any search termination condition in the search termination condition group. The determination of whether to terminate the search using the search termination condition set is specifically shown in fig. 7.
Wherein the set of search termination conditions comprises:
1. the searching times of the particle swarm are equal to the preset maximum iteration times;
2. searching to obtain a cost function of the optimal particle individuals in the particle swarm, wherein the cost function is larger than a preset threshold value;
3. the absolute difference degree of the cost function of the optimal particle individuals in the particle swarm of the adjacent generations (the typical value of the preset algebra can be 10, 15 and the like) is smaller than the preset deviation setting.
Embodiment 4, as shown in fig. 8, this embodiment discloses an apparatus for improving particle swarm optimization combined beam fitting for visible light wireless technology, including:
the importing unit is used for obtaining actual measurement data of the non-Lambert commercial solid-state light source, wherein the actual measurement data comprises actual measurement values generated by testing the radiation intensity of the non-Lambert commercial solid-state light source in different space directions point by point in a three-dimensional space;
the model building unit is used for building an initial non-Lambert commercial solid-state light source beam model;
the particle swarm optimization calculation unit is used for obtaining a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, optimizing the particle swarm by using a particle swarm optimization algorithm and outputting an optimal solution, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of the beam obtained by using actual measurement data of the non-Lambert commercial solid-state light source;
and the determining unit is used for determining the fitted non-Lambert commercial solid-state light source 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 execute, when running, a method for improving particle swarm optimization combined beam fitting for visible light wireless technology.
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 by the processor and executed to implement a method for improving particle swarm optimization combined beam fitting for visible light wireless technology.
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 (10)
1. A method for improving particle swarm optimization combined beam fitting for a visible light wireless technology is characterized by comprising the following steps:
obtaining actual measurement data of the non-Lambert commercial solid-state light source, wherein the actual measurement data comprises actual measurement values generated by point-by-point testing of the radiation intensity of the non-Lambert commercial solid-state light source in different space directions in a three-dimensional space;
constructing an initial non-Lambertian commercial solid-state light source beam model;
obtaining a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, optimizing the particle swarm by using a particle swarm optimization algorithm, and outputting an optimal solution, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of the obtained beam by using actual measurement data of the non-Lambert commercial solid-state light source;
and determining the fitted non-Lambertian commercial solid-state light source beam model according to the optimal solution.
2. The method of claim 1, wherein the initial non-lambertian commercial solid state light source beam model comprises:
the initial non-lambertian commercial solid-state light source beam model, if its beam is circularly symmetric, 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; a. the1i、A2i、A3iThe amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function are respectively; b is1j、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 initial non-lambertian commercial solid-state light source beam model, if its beam is non-circularly symmetric and the beam edge is smooth, is as follows:
wherein theta is the pitch angle of the emergent direction of the light beam, and phi is the azimuth 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; a. the1i、A2i、A3iThe amplitude coefficient, the phase offset coefficient and the power coefficient of the ith cosine power function are respectively; b is1j、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 initial non-lambertian commercial solid-state light source beam model, if its beam is non-circularly symmetric and the beam edge is sharp, is as follows:
wherein theta is the pitch angle of the emergent direction of the light beam, and phi is the azimuth angle of the emergent direction of the light beam; gi(theta) is Gi(θ)＝gi1-gi2exp[-gi3(|θ|-gi4)2](ii) a Step function UiIs composed of
Coefficient function gi5(phi) is
Peak angle function thetaip(phi) is
3. The method according to claim 1 or 2, wherein the obtaining a particle swarm according to an initial non-lambertian commercial solid-state light source beam model, optimizing the particle swarm using a particle swarm optimization algorithm, and outputting an optimal solution comprises:
constructing a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of beams obtained by utilizing actual measurement data of the non-Lambert commercial solid-state light source;
constructing a weight factor, a learning factor and a cost function;
performing heuristic search on the particle swarm according to the weight factor, the learning factor and the cost function;
and setting a search termination condition group, judging whether to terminate the search by using the search termination condition group, and terminating the search and outputting an optimal solution in response to meeting any search termination condition in the search termination condition group.
4. The method of claim 3, wherein the set of search termination conditions comprises:
the searching times of the particle swarm are equal to the preset maximum iteration times;
searching to obtain a cost function of the optimal particle individuals in the particle swarm, wherein the cost function is larger than a preset threshold value;
the absolute difference degree of the cost function of the optimal particle individuals in the adjacent generations of particle swarms is smaller than the preset deviation setting.
5. The method for improving particle swarm optimization combined beam fitting for visible light wireless technology according to claim 1 or 2, wherein the cost function comprises:
calculating deviation between the beam intensity of the fitted non-Lambert commercial solid-state light source beam model in each measured space direction and the corresponding beam intensity in the measured data, and cumulatively summing absolute values of the deviations;
accumulating and summing the intensity of each wave beam in the measured data;
calculating an absolute difference value between the accumulated sum result of the absolute values of the deviations and the accumulated sum result of the measured data;
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 method of claim 3, wherein the cost function comprises:
calculating deviation between the beam intensity of the fitted non-Lambert commercial solid-state light source beam model in each measured space direction and the corresponding beam intensity in the measured data, and cumulatively summing absolute values of the deviations;
accumulating and summing the intensity of each wave beam in the measured data;
calculating an absolute difference value between the accumulated sum result of the absolute values of the deviations and the accumulated sum result of the measured data;
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. The method of claim 4, wherein the cost function comprises:
calculating deviation between the beam intensity of the fitted non-Lambert commercial solid-state light source beam model in each measured space direction and the corresponding beam intensity in the measured data, and cumulatively summing absolute values of the deviations;
accumulating and summing the intensity of each wave beam in the measured data;
calculating an absolute difference value between the accumulated sum result of the absolute values of the deviations and the accumulated sum result of the measured data;
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.
8. A particle swarm optimization combined beam fitting device for improving visible light wireless technology is characterized by comprising:
the importing unit is used for obtaining actual measurement data of the non-Lambert commercial solid-state light source, wherein the actual measurement data comprises actual measurement values generated by testing the radiation intensity of the non-Lambert commercial solid-state light source in different space directions point by point in a three-dimensional space;
the model building unit is used for building an initial non-Lambert commercial solid-state light source beam model;
the particle swarm optimization calculation unit is used for obtaining a particle swarm according to an initial non-Lambert commercial solid-state light source beam model, optimizing the particle swarm by using a particle swarm optimization algorithm and outputting an optimal solution, wherein the particle swarm comprises a plurality of particles, and the particles are one-dimensional vectors corresponding to correlation coefficients of a plurality of functions of the obtained beam by using actual measurement data of the non-Lambert commercial solid-state light source;
and the determining unit is used for determining the fitted non-Lambert commercial solid-state light source beam model according to the optimal solution.
9. A storage medium having stored thereon a computer program readable by a computer, the computer program being arranged to perform the method for improving particle swarm optimization combined beam fitting for visible light wireless technology as claimed in any one of claims 1 to 7 when running.
10. An electronic device, comprising a processor and a memory, wherein a computer program is stored in the memory, and wherein the computer program is loaded and executed by the processor to implement the method for improving particle swarm optimization combined beam fitting for visible light wireless technology as claimed in any one of claims 1 to 7.
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