CN111880402A - Method and device for controlling product parameters of fluorescent powder layer and storage medium - Google Patents
Method and device for controlling product parameters of fluorescent powder layer and storage medium Download PDFInfo
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Abstract
The invention discloses a method and a device for controlling the product parameters of a fluorescent powder layer and a storage medium, wherein the method for controlling the product parameters of the fluorescent powder layer comprises the following steps: determining the optimal value of the process parameter by using a moth fire suppression algorithm, wherein the type of the process parameter is determined by the type of the product parameter, and applying the optimal value of the process parameter in the coating process of the fluorescent powder layer so as to control the value of the product parameter. The method for controlling the parameters of the fluorescent powder layer product does not depend on manual experience judgment, has small error, and can readjust the values of the process parameters at low cost and high speed after the coating process is updated, thereby being suitable for new process requirements. The invention is widely applied to the technical field of fluorescent powder coating.
Description
Technical Field
The invention relates to the technical field of fluorescent powder coating, in particular to a method and a device for controlling the product parameters of a fluorescent powder layer and a storage medium.
Background
The fluorescent powder coating technology is widely applied to the fields of LED production and the like. The coating quality of the fluorescent powder layer in the LED can be embodied by product parameters such as the thickness and uniformity of the fluorescent powder layer. The thickness and uniformity of the phosphor layer may affect the luminous efficiency, the luminous color uniformity and the chromaticity uniformity of the LED, and thus, the product parameters such as the thickness and uniformity of the phosphor layer coated in the LED need to be well controlled.
The product parameters of the fluorescent powder layer belong to the result of applying the coating process, and the value of the process parameters applied in the coating process influences the value of the product parameters of the finally obtained fluorescent powder layer. Therefore, the control of the product parameters of the fluorescent powder layer can be converted into the control of the process parameters in the coating process, namely, the values of the process parameters are determined and applied in the coating process. The prior art generally adjusts the values of the process parameters through the experience of a technician, so that the prior art generally has large errors, and the prior art is not suitable for a new process after the coating process is updated, so that the process parameters need to be readjusted at high cost.
Disclosure of Invention
In view of at least one of the above technical problems, an object of the present invention is to provide a method, an apparatus and a storage medium for controlling parameters of a phosphor layer product.
In one aspect, an embodiment of the present invention includes a method for controlling parameters of a phosphor layer product, including:
determining the optimal value of the process parameter by using a moth fire suppression algorithm; the type of the process parameter is determined by the type of the product parameter;
and applying the optimal value of the process parameter in the coating process of the fluorescent powder layer so as to control the value of the product parameter.
Further, the product parameters include thickness and uniformity of the phosphor layer; the process parameters comprise fluorescent powder coating glue flow rate, fluorescent powder coating time, fluorescent powder coating gas-liquid flow rate and fluorescent powder coating temperature.
Further, the moth fire suppression algorithm is initialized by the process parameters; the method for determining the optimal value of the process parameter by using the moth fire suppression algorithm specifically comprises the following steps:
executing at least one iterative process of the moth fire suppression algorithm; each of the iterative processes includes:
updating the moth population through an ODE mechanism;
generating a flame population according to the fitness value of the moth population; the fitness value is determined by a fitness function, and the fitness function is determined by measured values and given values of the product parameters;
updating the position of each moth individual through spiral flight of the moth individual to the flame individual;
dividing the moth individuals into corresponding sub-populations according to the fitness values of the moth individuals;
updating the worst moth individuals in each sub-population respectively based on a mixed frog-leaping local search mechanism;
updating the moth population by a death mechanism;
according to the condition of meeting the iteration ending condition, switching to the next iteration process or outputting a global optimal solution; the global optimal solution is an optimal value of the process parameter.
Further, the updating of the moth population by the ODE mechanism specifically includes:
updating the position of the moth population through a reverse learning algorithm;
and carrying out variation, crossing and selection on the moth population through a differential evolution algorithm.
Further, generating a fire population according to the fitness value of the moth population specifically comprises:
determining the individual number of flames according to the accumulated times of the iterative process, the preset maximum iterative times and the preset initial number of flame populations;
when the iterative process is the first iterative process, selecting a plurality of moth individuals with the highest fitness value from all the moth individuals to form the flame population, otherwise, selecting a plurality of moth individuals with the highest fitness value from a mixed set to form the flame population, wherein the mixed set comprises all the moth individuals and the flame individuals obtained in the last iterative process, and the size of the flame population is the number of the flame individuals.
Further, the positions of the moth individuals are updated through spiral flight from the moth individuals to the flame individuals, and the formula used is as follows:
S(xi-Fj)＝Di*ebk*cos(2πt)+Fj；Di＝|xi-Fj|；
wherein x isiIs the ith moth individual in the moth population, FjFor the jth individual flame in the flame population, DiIs xiAnd FjB is a logarithmic spiral constant, k is [ -1,1]The random number of (1).
Further, the dividing the moth individuals into corresponding sub-populations according to the fitness value of the moth individuals specifically comprises:
respectively determining the number of each moth individual according to the sequence of the fitness values from small to large;
respectively determining the number of each sub-population;
dividing each moth individual into a sub-population; wherein, for the sub-population with the largest number, the number of the divided moth individuals is a multiple of the number of the sub-population; for the rest of the sub-populations, the number of the sub-population is the remainder of the number of the moth individuals divided into the sub-population to the total number of the sub-populations.
Further, the updating the moth population through a death mechanism specifically includes:
acquiring the sum of fitness values of all the moth individuals; the sum of the fitness values is the sum of the fitness values determined by the moth individuals of the latest generations;
setting a plurality of the moth individuals with the largest sum of the fitness values as dead individuals;
the dead individual will be updated by a number of the individual moths with the smallest sum of the fitness values by the following Levy flight formula:
xre＝xnd+Levy(x)*xnd；
wherein xreTo the initial position of the regenerated moth individual, xndFor non-deceased individuals as opposed to said deceased individuals, Levy (x) is a random walk function, u and v are parameters satisfying a standard normal distribution, u to N (0,1), v to N (0,1), (x) is a gamma function, and β is a parameter.
In another aspect, an embodiment of the present invention further includes a computer apparatus, including a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to perform the method of the embodiment.
In another aspect, the present invention further includes a storage medium, in which a program executable by a processor is stored, and the program executable by the processor is used for executing the method of the embodiment when being executed by the processor.
The invention has the beneficial effects that: the method for controlling the parameters of the fluorescent powder layer product in the embodiment does not depend on manual experience judgment, has small errors, and can readjust the values of the process parameters at low cost and high speed after the coating process is updated, so that the method is suitable for new process requirements.
Drawings
Fig. 1 is a schematic diagram of a parameter control method of a phosphor layer product in an embodiment.
Detailed Description
In this embodiment, the method for controlling the product parameters of the phosphor layer includes the following steps:
s1, determining an optimal value of a process parameter by using a moth fire suppression algorithm; the type of the process parameter is determined by the type of the product parameter;
and S2, applying the optimal value of the process parameter in the coating process of the fluorescent powder layer so as to control the value of the product parameter.
In the present embodiment, the principle of steps S1 and S2 is shown in fig. 1.
Before step S1 is executed, the type of the process parameter to be controlled is determined, taking the coating process of the phosphor layer in the LED as an example, since the quality of the coating process is mainly reflected by the product parameters such as the thickness and uniformity of the phosphor layer, and the product parameters such as the thickness and uniformity of the phosphor layer are mainly affected by the process parameters such as the flow rate of the phosphor coating glue, the phosphor coating time, the flow rate of the phosphor coating gas and liquid, and the phosphor coating temperature applied in the coating process, in this embodiment, the process parameters such as the flow rate of the phosphor coating glue, the phosphor coating time, the flow rate of the phosphor coating gas and liquid, and the phosphor coating temperature are determined as the process parameters to be controlled, i.e., the process parameters to be determined by the moth-fire-fighting algorithm.
Before executing step S1, the moth fire suppression algorithm is initialized. In this embodiment, each basic parameter of the moth fire suppression optimization algorithm is set: the number of moth populations is 30, the initial number of flame populations is 30, the maximum iteration number is 1000, and the logarithmic spiral constant is 1.
And then initializing a moth population, and calculating the fitness value of each moth individual at the initial moment through a fitness function. In this embodiment, the fitness function is determined by the measured value and the given value of the product parameter, and specifically, the formula of the fitness function is as follows:
in the formula, realtRepresenting measured values of the thickness of the phosphor layer, pretA given value representing the thickness of the phosphor layer; realuMeasured value, pre, representing the uniformity of the phosphor layeruA given value representing the uniformity of the phosphor layer. In this embodiment, pretThe value can be 6mm, preuThe value may be 100 x 10-5mm2。
In this embodiment, the measured value of the thickness of the phosphor layer and the measured value of the uniformity of the phosphor layer may be obtained from the measurement of the trial product of the phosphor layer.
In this embodiment, the initialization formula used for initializing the moth population is as follows:
xn＝rand*(ub-lb)+lb。
in the formula, xnThe range of rand is [0,1 ] for moth population]Ub is the upper bound of the search interval, lb is the lower bound of the search interval, and the search interval is the value range of the parameter to be optimized. In the example, the flow rate of the fluorescent powder coating glue solution ranges from 0m/s to 10m/s, the coating time of the fluorescent powder ranges from 0s to 100s, the flow rate of the fluorescent powder coating gas solution ranges from 0m/s to 10m/s, and the coating temperature of the fluorescent powder ranges from 25 ℃ to 75 ℃.
After initialization of the moth fire suppression algorithm is completed, the moth fire suppression algorithm may be executed. In this embodiment, the moth fire suppression algorithm includes a plurality of iterative processes, and one of the iterative processes is described. Referring to fig. 1, each iterative process includes the following steps:
A1. updating the moth population through an ODE mechanism;
A2. generating a flame population according to the fitness value of the moth population;
A3. updating the positions of all the moth individuals through the spiral flight of the moth individuals to the flame individuals;
A4. dividing the moth individuals into corresponding sub-populations according to the fitness value of the moth individuals;
A5. updating the worst moth individuals in each sub-population respectively based on a mixed frog-leaping local search mechanism;
A6. updating the moth population through a death mechanism;
A7. and switching to the next iteration process or outputting a global optimal solution according to the condition of meeting the iteration ending condition, and determining the global optimal solution as the optimal value of the process parameter.
In this embodiment, step a1, namely the step of updating the moth population through the ODE mechanism, specifically includes:
A101. updating the position of the moth population through a reverse learning algorithm;
A102. and (3) carrying out variation, crossing and selection on the moth population through a differential evolution algorithm.
In this embodiment, the ODE mechanism is a combination of a reverse learning algorithm and a differential evolution algorithm, and can be used to obtain a high-quality population.
When step a101 is executed, the positions of the moth populations are updated by using a reverse learning algorithm, and the formula used is as follows:
xnew＝ub+lb-xn；
wherein xnewAnd updating the new position of the moth population for the reverse learning algorithm.
In step a102, the moth population is updated by using the variation, crossover and selection mechanism of the differential evolution algorithm.
In the mutation mechanism, the mutation formula is:
in the formula (I), the compound is shown in the specification,
represents the new individual after variation of the ith moth individual in the t generation moth population, wherein r1, r2 and r3 are three unequal numbers, r1, r2 and r 3E [1, n ]]R represents a scaling factor of mutation, and the value range of R is [0,1 ]]。
In the interleaving mechanism, the interleaving formula used is:
in the formula (I), the compound is shown in the specification,
the cross probability is pc. In this embodiment, the crossover probability may be set to 0.5.
In the selection mechanism, the selection formula used is:
in the formula, f () represents a fitness function.
In this embodiment, the step a2, namely the step of generating the flame population according to the fitness value of the moth population, specifically includes:
A201. determining the individual number of flames according to the accumulated times of the iterative process, the preset maximum iterative times and the preset initial number of flame populations;
A202. when the iterative process is the first iterative process, selecting a plurality of moth individuals with the highest fitness value from all the moth individuals to form the flame population, otherwise, selecting a plurality of moth individuals with the highest fitness value from a mixed set to form the flame population, wherein the mixed set comprises all the moth individuals and the flame individuals obtained in the last iterative process, and the size of the flame population is the number of the flame individuals.
In this embodiment, the formula used for executing step a201 is:
in the formula, frame _ no represents the number of flame individuals obtained in the current iteration process, N is a preset flame population initial number, l represents the current iteration number, namely the accumulated number of executed iteration processes including the current iteration process, T represents a preset maximum iteration number, and round () represents a rounding function. Because the moth fire suppression algorithm used in the embodiment is composed of a plurality of iteration processes, when the moth fire suppression algorithm is executed by using a computer program, each iteration process can be counted, and the accumulated times of the iteration processes are determined by reading the counts.
In this embodiment, when step a202 is executed, it may be determined whether the current iteration process is the first iteration process by counting the number of iterations. When the accumulated times of the iteration process is 1, the iteration process is shown as the first iteration process, the moth populations are subjected to fitness value sorting, and the moth individuals with the minimum fitness value and the number of the individual flame numbers are selected as the individual flame of the iteration process; when the accumulated times of the iteration process is more than 1, the iteration process is not the first iteration process, moth individuals and flame individuals obtained in the last iteration process form a mixed set, then the moth individuals and the flame individuals in the mixed set are sequenced according to the sequence of the fitness values from small to large, and the individuals with the minimum fitness value, namely the number of the flame individuals and the number of the flame individuals, are selected as the flame individuals in the iteration process, so that the selected flame individuals can be either the moth individuals or the flame individuals obtained in the last iteration process.
In this embodiment, in step a3, that is, the step of updating the position of each moth individual through the spiral flight from the moth individual to the flame individual, the formula used is:
S(xi,Fj)＝Di*ebk*cos(2πt)+Fj；Di＝|xi-Fj|；
wherein x isiIs the ith moth individual in the moth population, FjIs the jth individual in the fire population, i.e., moth individual x in this exampleiWith individual flames FjMatch, S (x)i,Fj) To the updated location of the moth individual, DiIs xiAnd FjB is a logarithmic spiral constant, k is [ -1,1]The random number of (1).
In this embodiment, the step a4, namely the step of dividing the moth individuals into corresponding sub-populations according to the fitness values of the moth individuals, specifically includes:
A401. respectively determining the number of each moth individual according to the sequence of the fitness values from small to large;
A402. respectively determining the number of each sub-population;
A403. dividing each moth individual into a sub-population; wherein, for the sub-population with the largest number, the number of the divided moth individuals is a multiple of the number of the sub-population; for the rest of the sub-populations, the number of the sub-population is the remainder of the number of the moth individuals divided into the sub-population to the total number of the sub-populations.
In step a401, if there are m moth individuals, the moth individuals may be numbered 1 and 2 … … m. In step a402, the total number of sub-populations may be set to 6, i.e. the numbers of the respective sub-populations are 1, 2, 3, 4, 5 and 6.
When step a403 is executed, the moth individuals assigned to the sub-population having the largest number, that is, the sub-population having the number 6, have the numbers 6, 12, 18 … …, and the like. For other sub-populations, the remainder of the moth individual number to the total number of sub-populations is determined, and the number of the sub-population to which the moth individual is to be assigned is determined based on the remainder, for example, for the moth individual number 15, the moth individual number 15 is assigned to the sub-population number 3 because 15 ÷ 6 ═ 2 … … 3.
The allocation of step a403 may also be replaced by another allocation with the same effect: and sequentially dividing the ordered moth individuals into sub-populations according to an ordering sequence, wherein the moth individual with the minimum fitness value is divided into a first sub-population, then the moth individual with the minimum fitness value is divided into a second sub-population, and so on, dividing the moth individual with the minimum fitness value ordered m into the mth group, and dividing the next moth individual, namely the moth individual with the minimum fitness ordered m +1, into the first sub-population, and repeating the steps until all moth individuals are divided into the corresponding sub-populations.
In this embodiment, in step a5, that is, the step of updating the worst moth individuals in each sub-population based on the local mixed frog-leaping search mechanism, the formula used may be any of the following:
xupdate＝2*rand*(Pb-Pw)+Pw；
xupdate＝2*rand*(Pg-Pw)+Pw；
xupdate＝rand*((Pb+Pg)/2-Pw)+Pw；
in the formula xupdateIndicating updatedWorst moth individual, PbRepresenting the optimal moth individual in a sub-population, PwRepresents the worst moth individual, P, in a sub-populationgRepresents globally optimal individuals, i.e. optimal moth individuals determined by comparing all sub-populations together. In this embodiment, "optimal" may mean having the smallest fitness value, and "worst" may mean having the largest fitness value.
In this embodiment, the step a6, namely the step of updating the moth population through a death mechanism, specifically includes:
A601. acquiring the sum of fitness values of all moth individuals; wherein the sum of the fitness values is the sum of the fitness values determined by the moths of the latest generations;
A602. setting a plurality of moth individuals with the maximum fitness value sum as dead individuals;
A603. the dead individual will be updated by several moth individuals with the smallest sum of fitness values by flying in the formula of lewy:
xre＝xnd+Levy(x)*xnd；
wherein xreTo the initial position of the regenerated moth individual, xndFor non-deceased individuals as opposed to said deceased individuals, Levy (x) is a random walk function, u and v are parameters satisfying a standard normal distribution, u to N (0,1), v to N (0,1), (x) is a gamma function, and β is a parameter.
In this embodiment, when step a601 is executed, death threshold L may be set, and in this embodiment, L is set to 10. And acquiring the sum of the fitness values determined by each moth individual in the latest L generation as the sum of the fitness values.
In this embodiment, when step a602 is executed, the death number Dn may be set, and Dn is set to 15 in this embodiment.
And setting Dn moth individuals with the largest sum of fitness values as dead individuals. For these Dn dead individuals, step A603 will be performed by several moth individuals with the smallest sum of fitness values, with the Levy flight formula xre＝xnd+Levy(x)*xndPerforming an update, wherein xreIs the initial position of the regenerated moth individual.
In this embodiment, in an iteration process, after the steps a1-a6 are executed, a7 is executed to determine whether an iteration end condition is met, where the iteration end condition may be set to reach a preset maximum iteration number or an error precision is smaller than a preset threshold. If the iteration end condition is satisfied, a global optimal solution is output, and the output global optimal solution is the optimal value of the process parameter to be determined by executing the step S1. And if the iteration end condition is not met, switching to the next iteration process, and re-executing the steps A1-A7.
In the present embodiment, the optimum value of the process parameter determined by performing step S1 may be applied to the coating process, that is, the relevant parameter in the coating process is set to the optimum value of the process parameter determined by performing step S1. Under the condition that other production conditions of the coating process are not changed, the product parameters of the fluorescent powder layer produced by the coating process are related to the process parameters, and when the process parameters are configured to the optimal values in the embodiment, the product parameters of the produced fluorescent powder layer can be optimized, so that the production of high-quality LED products is facilitated. The method for controlling the parameters of the fluorescent powder layer product in the embodiment does not depend on manual experience judgment, has small errors, and can readjust the values of the process parameters at low cost and high speed after the coating process is updated, so that the method is suitable for new process requirements.
In this embodiment, the moth fire suppression algorithm used is an improved moth fire suppression algorithm. Firstly, a high-quality moth population is obtained by utilizing a reverse learning algorithm, then the diversity of the moth population is enhanced by utilizing a differential evolution algorithm, and the global search capability of the algorithm is greatly improved. In addition, the moth fire suppression algorithm used in the embodiment applies an improved mixed frog leap local search algorithm and a death mechanism as an enhanced local search algorithm for eliminating moth individuals with low fitness values, so that the moth fire suppression algorithm used in the embodiment can easily jump out of local optimums, and further improves the convergence rate.
In this embodiment, a computer device includes a memory and a processor, where the memory is used to store at least one program, and the processor is used to load the at least one program to execute the phosphor layer product parameter control method in the embodiment, so as to achieve the same technical effects as those described in the embodiment.
In this embodiment, a storage medium has stored therein a processor-executable program, which when executed by a processor is configured to perform the phosphor layer product parameter control method in the embodiments, achieves the same technical effects as described in the embodiments.
It should be noted that, unless otherwise specified, when a feature is referred to as being "fixed" or "connected" to another feature, it may be directly fixed or connected to the other feature or indirectly fixed or connected to the other feature. Furthermore, the descriptions of upper, lower, left, right, etc. used in the present disclosure are only relative to the mutual positional relationship of the constituent parts of the present disclosure in the drawings. As used in this disclosure, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. In addition, unless defined otherwise, all technical and scientific terms used in this example have the same meaning as commonly understood by one of ordinary skill in the art. The terminology used in the description of the embodiments herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used in this embodiment, the term "and/or" includes any combination of one or more of the associated listed items.
It will be understood that, although the terms first, second, third, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element of the same type from another. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of the present disclosure. The use of any and all examples, or exemplary language ("e.g.," such as "or the like") provided with this embodiment is intended merely to better illuminate embodiments of the invention and does not pose a limitation on the scope of the invention unless otherwise claimed.
It should be recognized that embodiments of the present invention can be realized and implemented by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer readable memory. The methods may be implemented in a computer program using standard programming techniques, including a non-transitory computer-readable storage medium configured with the computer program, where the storage medium so configured causes a computer to operate in a specific and predefined manner, according to the methods and figures described in the detailed description. Each program may be implemented in a high level procedural or object oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language. Furthermore, the program can be run on a programmed application specific integrated circuit for this purpose.
Further, operations of processes described in this embodiment can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described in this embodiment (or variations and/or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) collectively executed on one or more processors, by hardware, or combinations thereof. The computer program includes a plurality of instructions executable by one or more processors.
Further, the method may be implemented in any type of computing platform operatively connected to a suitable interface, including but not limited to a personal computer, mini computer, mainframe, workstation, networked or distributed computing environment, separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, and the like. Aspects of the invention may be embodied in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, optically read and/or write storage medium, RAM, ROM, or the like, such that it may be read by a programmable computer, which when read by the storage medium or device, is operative to configure and operate the computer to perform the procedures described herein. Further, the machine-readable code, or portions thereof, may be transmitted over a wired or wireless network. The invention described in this embodiment includes these and other different types of non-transitory computer-readable storage media when such media include instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor. The invention also includes the computer itself when programmed according to the methods and techniques described herein.
A computer program can be applied to input data to perform the functions described in the present embodiment to convert the input data to generate output data that is stored to a non-volatile memory. The output information may also be applied to one or more output devices, such as a display. In a preferred embodiment of the invention, the transformed data represents physical and tangible objects, including particular visual depictions of physical and tangible objects produced on a display.
The above description is only a preferred embodiment of the present invention, and the present invention is not limited to the above embodiment, and any modifications, equivalent substitutions, improvements, etc. within the spirit and principle of the present invention should be included in the protection scope of the present invention as long as the technical effects of the present invention are achieved by the same means. The invention is capable of other modifications and variations in its technical solution and/or its implementation, within the scope of protection of the invention.
Claims (10)
1. A method for controlling the product parameters of a fluorescent powder layer is characterized by comprising the following steps:
determining the optimal value of the process parameter by using a moth fire suppression algorithm; the type of the process parameter is determined by the type of the product parameter;
and applying the optimal value of the process parameter in the coating process of the fluorescent powder layer so as to control the value of the product parameter.
2. The phosphor layer product parameter control method according to claim 1, wherein said product parameters include thickness and uniformity of said phosphor layer; the process parameters comprise fluorescent powder coating glue flow rate, fluorescent powder coating time, fluorescent powder coating gas-liquid flow rate and fluorescent powder coating temperature.
3. The method for controlling the parameters of the fluorescent powder layer product according to claim 1 or 2, wherein the moth fire suppression algorithm is initialized by the process parameters; the method for determining the optimal value of the process parameter by using the moth fire suppression algorithm specifically comprises the following steps:
executing at least one iterative process of the moth fire suppression algorithm; each of the iterative processes includes:
updating the moth population through an ODE mechanism;
generating a flame population according to the fitness value of the moth population; the fitness value is determined by a fitness function, and the fitness function is determined by measured values and given values of the product parameters;
updating the position of each moth individual through spiral flight of the moth individual to the flame individual;
dividing the moth individuals into corresponding sub-populations according to the fitness values of the moth individuals;
updating the worst moth individuals in each sub-population respectively based on a mixed frog-leaping local search mechanism;
updating the moth population by a death mechanism;
according to the condition of meeting the iteration ending condition, switching to the next iteration process or outputting a global optimal solution; the global optimal solution is an optimal value of the process parameter.
4. The method for controlling the parameters of the phosphor layer product according to claim 3, wherein the updating of the population of moths by the ODE mechanism specifically comprises:
updating the position of the moth population through a reverse learning algorithm;
and carrying out variation, crossing and selection on the moth population through a differential evolution algorithm.
5. The method for controlling the parameters of the phosphor layer product according to claim 3, wherein generating a flame population according to the fitness value of the moth population specifically comprises:
determining the individual number of flames according to the accumulated times of the iterative process, the preset maximum iterative times and the preset initial number of flame populations;
when the iterative process is the first iterative process, selecting a plurality of moth individuals with the highest fitness value from all the moth individuals to form the flame population, otherwise, selecting a plurality of moth individuals with the highest fitness value from a mixed set to form the flame population, wherein the mixed set comprises all the moth individuals and the flame individuals obtained in the last iterative process, and the size of the flame population is the number of the flame individuals.
6. The method for controlling the parameters of the phosphor layer product according to claim 3, wherein the position of each of the moth individuals is updated by the spiral flight of the moth individual to the flame individual, and the formula used is as follows:
S(xi-Fj)＝Di*ebk*cos(2πt)+Fj；Di＝|xi-Fj|；
wherein x isiIs the ith moth individual in the moth population, FjFor the jth individual flame in the flame population, DiIs xiAnd FjB is a logarithmic spiral constant, k is [ -1,1]The random number of (1).
7. The method for controlling the parameters of the phosphor layer product according to claim 3, wherein the dividing of the moth individuals into corresponding sub-populations according to the fitness values of the moth individuals specifically comprises:
respectively determining the number of each moth individual according to the sequence of the fitness values from small to large;
respectively determining the number of each sub-population;
dividing each moth individual into a sub-population; wherein, for the sub-population with the largest number, the number of the divided moth individuals is a multiple of the number of the sub-population; for the rest of the sub-populations, the number of the sub-population is the remainder of the number of the moth individuals divided into the sub-population to the total number of the sub-populations.
8. The method for controlling parameters of a phosphor layer product according to claim 3, wherein said updating said population of moths by a death mechanism specifically comprises:
acquiring the sum of fitness values of all the moth individuals; the sum of the fitness values is the sum of the fitness values determined by the moth individuals of the latest generations;
setting a plurality of the moth individuals with the largest sum of the fitness values as dead individuals;
the dead individual will be updated by a number of the individual moths with the smallest sum of the fitness values by the following Levy flight formula:
xre＝xnd+Levy(x)*xnd；
wherein xreFor regenerated mothInitial position of body, xndFor non-deceased individuals as opposed to said deceased individuals, Levy (x) is a random walk function, u and v are parameters satisfying a standard normal distribution, u to N (0,1), v to N (0,1), (x) is a gamma function, and β is a parameter.
9. A computer apparatus comprising a memory for storing at least one program and a processor for loading the at least one program to perform the method of any one of claims 1-8.
10. A storage medium having stored therein a program executable by a processor, wherein the program executable by the processor is adapted to perform the method of any one of claims 1-8 when executed by the processor.
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