CN111860987B - Method and device for predicting emission spectrum of mixed fluorescent material - Google Patents
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Abstract
The invention provides a method and a device for predicting emission spectrum of a mixed fluorescent material, wherein the method comprises the following steps: respectively obtaining absorption spectrum, emission spectrum and quantum efficiency of N fluorescent materials, wherein N is an integer greater than or equal to 2; obtaining the proportion of each fluorescent material mixed by N fluorescent materials; calculating the proportion coefficient of the emission spectrum of the N fluorescent materials mixed according to any proportion; establishing a neural network prediction model according to the proportion of each fluorescent material and the proportion coefficient of the emission spectrum; and obtaining an emission spectrum proportionality coefficient through a neural network prediction model, and predicting the emission spectrum of the mixed N fluorescent materials according to the emission spectrum proportionality coefficient obtained through the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient. According to the invention, the modified Bill model is combined with the artificial neural network to predict the emission spectrum of the mixed fluorescent powder material, so that the complex experimental test is replaced, and the labor and time cost are saved.
Description
Technical Field
The invention relates to the technical field of semiconductors, in particular to a mixed fluorescent material emission spectrum prediction method and a mixed fluorescent material emission spectrum prediction device.
Background
At present, the most commonly used white Light LED (Light-Emitting Diode) is generally a blue Light chip surface coated with a yellow fluorescent powder and silica gel composite material, and the method has the advantages of simple process and good reliability, but the white Light LED formed by combining blue Light excited yellow fluorescent powder is poor in color rendering performance due to the lack of a red Light part. In order to solve the above problems, a technical solution for improving the color rendering index of white LED lighting by mixing multicolor fluorescent powder is proposed, and the emission spectrum of the fluorescent material is one of the important indexes for representing the performance of the fluorescent material. At present, the emission spectra of various mixed fluorescent materials are tested by an instrument, which consumes labor and time, and particularly, when the proportion of each mixed fluorescent powder is regulated, repeated tests are required to be carried out on the mixed fluorescent materials. Therefore, the method has a certain guiding significance for researching the design of the high-quality white light LED light source by predicting the emission spectrum of the mixture of a plurality of fluorescent powders.
Disclosure of Invention
The invention provides a method for predicting the emission spectrum of a mixed fluorescent material, which can be used for predicting the emission spectrum of the mixed fluorescent material by combining a modified Bill lambertian model with an artificial neural network, replaces a redundant experimental test and saves labor and time cost.
The technical scheme adopted by the invention is as follows:
A method for predicting emission spectrum of a mixed fluorescent material comprises the following steps: respectively obtaining absorption spectrum, emission spectrum and quantum efficiency of N fluorescent materials, wherein N is an integer greater than or equal to 2; obtaining the proportion of each fluorescent material mixed by the N fluorescent materials; calculating the ratio coefficients of the emission spectrums of the N fluorescent materials mixed according to any proportion; establishing a neural network prediction model according to the proportion of each fluorescent material and the emission spectrum proportion coefficient; and obtaining an emission spectrum proportionality coefficient through the neural network prediction model, and predicting the emission spectrum of the mixed N fluorescent materials according to the emission spectrum proportionality coefficient obtained through the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient.
According to one embodiment of the present invention, the calculating the ratio coefficient of emission spectra of the N fluorescent materials after being mixed according to any ratio includes: and calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion based on the modified Bill lambertian model.
According to one embodiment of the present invention, calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion based on the modified beer lambertian model includes: calculating the absorption probability of mutual absorption among the fluorescent materials according to the absorption spectrum and the emission spectrum of each of the N fluorescent materials; and calculating the ratio coefficient of the emission spectrum of the N fluorescent materials mixed according to any ratio according to the proportion of each fluorescent material, the quantum efficiency and the absorption probability of mutual absorption among the fluorescent materials.
According to one embodiment of the invention, the emission spectrum scaling factor is generated by the following formula:
Wherein K i represents an emission spectrum proportionality coefficient, r i and r j represent proportions of fluorescent materials, q i and q j represent quantum efficiency of the fluorescent materials, δ ij represents probability that light emitted by the j-type fluorescent material is absorbed by the i-type fluorescent material, and δ ji represents probability that light emitted by the i-type fluorescent material is absorbed by the j-type fluorescent material.
According to one embodiment of the present invention, the absorption probability of the mutual absorption between each of the fluorescent materials is generated by the following formula:
Wherein ab i (λ) represents an i-type fluorescent material absorption spectrum, and em j (λ) represents a j-type fluorescent material emission spectrum.
According to one embodiment of the present invention, the method for building a neural network prediction model according to the proportion of each fluorescent material and the emission spectrum proportion coefficient includes: and taking the ratio of the emission spectrum proportionality coefficients of the N fluorescent materials mixed according to any proportion and the ratio of each fluorescent material as a data set of a neural network prediction model, and establishing the neural network prediction model, wherein the ratio of each fluorescent material is taken as an input variable of the neural network prediction model, and the ratio of the emission spectrum proportionality coefficients of the N fluorescent materials mixed according to any proportion is taken as an output variable.
According to one embodiment of the present invention, the predicted emission spectra of the mixed N fluorescent materials is generated by the following formula:
Wherein em eq (λ) represents the predicted emission spectrum after mixing the N fluorescent materials, k i 'represents the emission spectrum proportionality coefficient obtained by the neural network prediction model, and em (λ) represents the emission spectrum of the fluorescent material corresponding to k i'.
In addition, a mixed fluorescent material emission spectrum prediction device is also provided, which comprises: the first acquisition module is used for respectively acquiring the absorption spectrum, the emission spectrum and the quantum efficiency of N fluorescent materials, wherein N is an integer greater than or equal to 2; the second acquisition module is used for acquiring the proportion of each fluorescent material after the N fluorescent materials are mixed; the calculation module is used for calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion; the building module is used for building a neural network prediction model according to the proportion of each fluorescent material and the emission spectrum proportion coefficient; the prediction module is used for obtaining the emission spectrum proportionality coefficient through the neural network prediction model and predicting the emission spectrum of the mixed N fluorescent materials according to the emission spectrum proportionality coefficient obtained through the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient.
The invention has the beneficial effects that:
according to the invention, the method is popularized to a luminescent material system mixed by multiple kinds of fluorescent powder by correcting the Bill model, training and learning of the neural network are carried out according to the proportionality coefficient obtained by the corrected Bill model and the proportions of the fluorescent powder, the predicted value of the proportionality coefficient is obtained by applying a neural network algorithm, and then the predicted value of the proportionality coefficient is multiplied by the emission spectrum of the corresponding fluorescent powder and summed up, so that the prediction of the emission spectrum after the multiple kinds of fluorescent powder are mixed is realized, thereby replacing redundant experimental tests, saving manpower and time, and having a certain guiding significance for researching the design of the high-quality white light LED light source.
Drawings
FIG. 1 is a flow chart of a method flow for predicting emission spectra of a mixed fluorescent material according to an embodiment of the invention;
FIG. 2 is an absorption spectrum of each of three phosphors according to one embodiment of the invention;
FIG. 3 is an emission spectrum of each of three phosphors according to one embodiment of the invention;
FIG. 4 is a topology of a BP neural network prediction model according to one embodiment of the invention;
FIG. 5 is a graph showing the percentage error between the predicted and corrected Bill theoretical calculation scaling coefficients for a neural network, according to one embodiment of the invention;
FIG. 6 shows three phosphor ratios of 7 according to one embodiment of the invention: 1:2, comparing the experiment with a neural network predicted emission spectrum;
FIG. 7 shows three phosphor ratios of 5 according to one embodiment of the invention: 3:2, comparing the experiment with a neural network predicted emission spectrum;
FIG. 8 shows three phosphor ratios of 2 according to one embodiment of the invention: 2:6, comparing the experiment with a neural network predicted emission spectrum;
FIG. 9 shows three phosphor ratios of 2 according to one embodiment of the invention: 6:2, comparing the experiment with a neural network predicted emission spectrum;
FIG. 10 shows three phosphor ratios of 3 according to one embodiment of the invention: 3:4, comparing the experiment with a neural network predicted emission spectrum;
fig. 11 is a block schematic diagram of a mixed fluorescent material emission spectrum prediction apparatus according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
As shown in fig. 1, the method for predicting the emission spectrum of the mixed fluorescent material according to the embodiment of the invention may include the following steps:
S1, respectively obtaining absorption spectrum, emission spectrum and quantum efficiency of N fluorescent materials, wherein N is an integer greater than or equal to 2.
Wherein the fluorescent material may be a fluorescent powder, and the corresponding absorption spectrum may be represented by ab i (λ), i represents the absorption spectrum of the ith fluorescent material, for example, ab 1 (λ) represents the absorption spectrum of the first fluorescent material; the corresponding emission spectrum may be represented by em i (λ), i representing the emission spectrum of the i-th fluorescent material, e.g., em 1 (λ) representing the emission spectrum of the first fluorescent material; the corresponding quantum efficiency may be represented by q i, i representing the quantum efficiency of the ith fluorescent material, e.g., q 1 representing the quantum efficiency of the first fluorescent material.
S2, obtaining the proportion of each fluorescent material after the N fluorescent materials are mixed. It should be noted that the proportion of each mixed fluorescent material was recorded and stored at the time of mixing.
S3, calculating the ratio coefficient of the emission spectrum of the N fluorescent materials mixed according to any proportion.
In one embodiment of the present invention, calculating the ratio of emission spectra of N fluorescent materials mixed according to an arbitrary ratio includes: and calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion based on the modified Bill lambertian model.
It should be noted that, the beer lambertian model is only for a single fluorescent material light emitting system, and the quantum efficiency of a single fluorescent material and the mutual absorption between multiple fluorescent materials are considered, so that the beer lambertian model needs to be modified, for example, the absorption probability of the mutual absorption between the fluorescent materials is calculated first, as a possible implementation manner, the emission spectrum proportionality coefficient of the N fluorescent materials after being mixed according to any proportion is calculated based on the modified beer lambertian model, which includes:
S31, calculating the absorption probability of mutual absorption among the fluorescent materials according to the absorption spectrum and the emission spectrum of each of the N fluorescent materials.
Specifically, the absorption probability of the mutual absorption between the respective phosphors is calculated by giving the respective absorption spectra ab i (λ) and emission spectra em i (λ) of the N phosphors. For example, the absorption probability of mutual absorption between the i-type fluorescent material and the j-type fluorescent material, wherein the i-type fluorescent material absorbs blue light, the i-type fluorescent material absorbs light emitted by the j-type fluorescent material, and the j-type fluorescent material absorbs light emitted by the i-type fluorescent material, and the absorption probability δ ij of mutual absorption between the respective fluorescent powders is the probability of absorption of light emitted by the j-type fluorescent material by the i-type fluorescent material, that is, the overlapping portion between the i-type fluorescent material absorption spectrum ab i (λ) and the j-type fluorescent material emission spectrum em i (λ), is generated by the following formula (1):
Wherein ab i (λ) represents an i-type fluorescent material absorption spectrum, and em j (λ) represents a j-type fluorescent material emission spectrum.
S32, calculating the ratio coefficient of the emission spectrum of the N fluorescent materials mixed according to any ratio according to the proportion of each fluorescent material, the quantum efficiency and the absorption probability of mutual absorption among the fluorescent materials.
After the ratio r i of each single-color fluorescent powder, the quantum efficiency q i and the mutual absorption probability delta ij between the fluorescent materials are introduced, the emission spectrum ratio coefficient is generated by the following formula (2):
Wherein K i represents an emission spectrum proportionality coefficient, r i and r j represent proportions of fluorescent materials, q i and q j represent quantum efficiency of the fluorescent materials, δ ij represents probability that light emitted by the j-type fluorescent material is absorbed by the i-type fluorescent material, and δ ji represents probability that light emitted by the i-type fluorescent material is absorbed by the j-type fluorescent material.
It should be noted that, the calculation modes of the above formulas (1) and (2) may be implemented by programming software, for example, may be implemented by Matlab programming.
And S4, building a neural network prediction model according to the proportion of each fluorescent material and the proportion coefficient of the emission spectrum. The neural network prediction model may be a BP (Back-ProPagation Network, back propagation neural network) neural network prediction model.
According to one embodiment of the invention, the method for establishing the neural network prediction model according to the proportion of each fluorescent material and the proportion coefficient of the emission spectrum comprises the following steps: and taking the ratio of the emission spectrum of the N fluorescent materials mixed according to any ratio and the ratio of each fluorescent material as a data set of the neural network prediction model, and establishing the neural network prediction model, wherein the ratio of each fluorescent material is taken as an input variable of the neural network prediction model, and the ratio of the emission spectrum of the N fluorescent materials mixed according to any ratio is taken as an output variable.
Specifically, an input layer, an output layer, an hidden layer and a weight threshold of a BP neural network prediction model are set:
Input layer: taking the ratio r i between the fluorescent powders as an input variable, and assuming the number of nodes of an input layer to be m;
Output layer: taking the proportionality coefficient K i calculated in the step 3 as an output variable, and assuming the number of nodes of an output layer to be n;
Hidden layer: the number of hidden layer nodes is obtained by the following empirical formula (3):
wherein q is the number of hidden layer nodes, m is the number of input layer nodes, n is the number of output layer nodes, and a is an adjustment constant between 1 and 10;
Weight and threshold: and after the weight and the threshold value are preliminarily selected, correcting by an error back propagation principle.
Wherein, the activation function of neurons in the hidden layer and the output layer of the BP neural network adopts tansig type hyperbolic tangent S-shaped transfer function, and the expression is as follows:
It should be noted that, the normalization and inverse normalization processing of the input and output samples of the training set and the testing set can be implemented by using mapminamax functions in Matlab programming software.
In addition, after the iteration times, the training targets and the learning rate are set, the emission spectrum proportionality coefficient k i' can be predicted, and the creation and execution of the BP neural network can be realized in Matlab.
S5, acquiring an emission spectrum proportionality coefficient through a neural network prediction model, and predicting the emission spectrum of the mixed N fluorescent materials according to the emission spectrum proportionality coefficient acquired through the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient.
Specifically, the proportionality coefficient k i' predicted by the BP neural network is multiplied by the emission spectrum of the corresponding fluorescent powder and summed up, so that the equivalent emission spectrum after mixing multiple fluorescent powders can be obtained, and the prediction of the emission spectrum of the mixed fluorescent powder is realized. As one possible implementation, the emission spectrum of the fluorescent material mixed in N is predicted to be generated by the following formula (4):
Where em eq (λ) represents the predicted emission spectrum after mixing the N fluorescent materials, k i 'represents the emission spectrum scaling factor obtained by the neural network prediction model, and em (λ) represents the emission spectrum of the fluorescent material corresponding to k i'.
In order to describe the technical solution of the present invention in more detail, taking 3 kinds of fluorescent materials as examples, how to implement the prediction of the mixed emission spectrum of the 3 kinds of fluorescent materials will be described in detail.
3 Types of fluorescent powder (fluorescent material) are selected, namely yellow fluorescent powder YAG04, red fluorescent powder R6535 and green fluorescent powder G525, and are named as 1, 2 and 3. The absorption spectra (ab 1, ab2, ab 3) and the emission spectra (em 1, em2, em 3) of the three monochromatic phosphors were measured, respectively, and as shown in fig. 2 and 3, the absorption probability δ ij was calculated by using Matlab software through a formula.
The formula of the proportionality coefficient K i is expressed in a matrix form, wherein the quantum efficiency q 1、q2、q3 of the three kinds of fluorescent powder is respectively 0.75, 0.8 and 0.78, and the proportionality coefficient K i of 80 groups can be calculated according to the quantum efficiency of the three kinds of fluorescent powder, the 80 groups of different proportions of the three kinds of fluorescent powder and the mutual absorption probability among the 3 kinds of fluorescent powder. The matrix expression of the formula of the proportionality coefficient K i is as follows:
And taking the proportion r i of the 80 groups of fluorescent powder and the 80 groups of proportionality coefficient K i obtained by calculation as a data set of the BP neural network, wherein the training set is 75 groups, and the test set is 5 groups. The input layer variable was selected to be the ratio r i of 3 phosphors, so the number of input layer nodes was m=3. The output layer variable is the proportionality coefficient k i', so that the output layer node n=3, and the prediction result is optimal when the number of hidden layer nodes is 6, so that the number of hidden layer nodes is 6, and the topological diagram of the BP neural network prediction model in the embodiment is shown in fig. 4. The input variables for testing the five sets of data are shown in table 1 below:
TABLE 1
| Numbering device | r1 | r2 | r3 |
| 1 | 7 | 1 | 2 |
| 2 | 5 | 3 | 2 |
| 3 | 2 | 2 | 6 |
| 4 | 2 | 6 | 2 |
| 5 | 3 | 3 | 4 |
After the iteration times, the training targets and the learning rate are set, 5 groups of proportion coefficients serving as a test set are predicted through a BP neural network algorithm, the error percentage between the predicted proportion coefficients and the proportion coefficients calculated based on the corrected Bill theory is analyzed to obtain the maximum error percentage not exceeding 5%, and the average error is not exceeding 2.1%, as shown in fig. 5, wherein the error percentage formula is shown as the following formula:
Wherein E P is the error percentage, R is the proportionality coefficient calculated by the Bill theory, and P is the proportionality coefficient predicted by the neural network.
And (3) predicting through the BP neural network to obtain the proportionality coefficient corresponding to each group of test set, multiplying the proportionality coefficient by the emission spectrum of the corresponding fluorescent powder, and summing the proportionality coefficient to calculate the emission spectrum of the mixed fluorescent powder.
In order to verify the accuracy of the present application, the yellow phosphor YAG, the red phosphor R6535, and the green phosphor G525 are measured according to the ratios given in the above table 1, and are fully mixed, the emission spectrum of the mixed phosphor is measured by the apparatus, the emission spectrum predicted by the BP neural network is compared with the emission spectrum measured by the apparatus, and as can be seen from fig. 6 to 10, the emission spectrum predicted by the method of the present application has good coincidence with the emission spectrum measured by the apparatus. Therefore, the emission spectrum of the mixed fluorescent material can be well predicted by adopting the BP neural network.
In summary, the method for predicting the emission spectrum of the mixed fluorescent material is promoted to a system of a luminescent material mixed by multiple fluorescent powders by correcting the Bill model, training and learning of the neural network are carried out according to the proportionality coefficient obtained by correcting the Bill model and the proportion of each fluorescent powder, the predicted value of the proportionality coefficient is obtained by applying a neural network algorithm, and then the emission spectrum of the corresponding fluorescent powder is multiplied and summed, so that the prediction of the emission spectrum after the mixing of the multiple fluorescent powders is realized, thereby replacing redundant experimental tests, saving manpower and time, and having a certain guiding significance for researching the design of the high-quality white light LED light source.
Fig. 11 is a block schematic diagram of a mixed fluorescent material emission spectrum prediction apparatus according to an embodiment of the present invention.
As shown in fig. 11, the mixed fluorescent material emission spectrum prediction apparatus of the present invention may include: the system comprises a first acquisition module 10, a second acquisition module 20, a calculation module 30, a building module 40 and a prediction module 50.
The first obtaining module 10 is configured to obtain absorption spectra, emission spectra, and quantum efficiencies of N fluorescent materials, where N is an integer greater than or equal to 2. The second obtaining module 20 is configured to obtain the proportion of each of the N kinds of fluorescent materials after mixing. The calculating module 30 is configured to calculate the ratio coefficient of the emission spectrum of the N fluorescent materials mixed according to any ratio. The building module 40 is configured to build a neural network prediction model according to the proportion of each fluorescent material and the proportion coefficient of the emission spectrum. The prediction module 50 is configured to obtain an emission spectrum scaling factor through a neural network prediction model, and predict an emission spectrum of the mixed N fluorescent materials according to the emission spectrum scaling factor obtained through the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum scaling factor.
According to one embodiment of the present invention, the calculation module 30 calculates the ratio of the emission spectra of the N fluorescent materials mixed according to any ratio, and is specifically configured to calculate the ratio of the emission spectra of the N fluorescent materials mixed according to any ratio based on the modified beer lambertian model.
According to one embodiment of the present invention, the calculating module 30 is further configured to calculate an absorption probability of mutual absorption between each of the N fluorescent materials according to respective absorption spectra and emission spectra of the fluorescent materials; and calculating the ratio coefficient of the emission spectrum of the N fluorescent materials mixed according to any ratio according to the proportion of each fluorescent material, the quantum efficiency and the absorption probability of mutual absorption among the fluorescent materials.
According to one embodiment of the invention, the emission spectral scaling factor is generated by the following formula:
Wherein K i represents an emission spectrum proportionality coefficient, r i and r j represent proportions of fluorescent materials, q i and q j represent quantum efficiency of the fluorescent materials, δ ij represents probability that light emitted by the j-type fluorescent material is absorbed by the i-type fluorescent material, and δ ji represents probability that light emitted by the i-type fluorescent material is absorbed by the j-type fluorescent material.
According to one embodiment of the present invention, the absorption probability of mutual absorption between each fluorescent material is generated by the following formula:
Wherein ab i (λ) represents an i-type fluorescent material absorption spectrum, and em j (λ) represents a j-type fluorescent material emission spectrum.
According to one embodiment of the present invention, the building module 40 builds a neural network prediction model according to the proportion of each fluorescent material and the proportion of the emission spectrum, and specifically is configured to build the neural network prediction model by using the proportion of the emission spectrum of the mixed N fluorescent materials according to any proportion and the proportion of each fluorescent material as a data set of the neural network prediction model, where the proportion of each fluorescent material is used as an input variable of the neural network prediction model, and the proportion of the emission spectrum of the mixed N fluorescent materials according to any proportion is used as an output variable.
According to one embodiment of the present invention, the prediction module 50 predicts the emission spectrum of the N fluorescent materials after mixing by the following formula:
Where em eq (λ) represents the predicted emission spectrum after mixing the N fluorescent materials, k i 'represents the emission spectrum scaling factor obtained by the neural network prediction model, and em (λ) represents the emission spectrum of the fluorescent material corresponding to k i'.
It should be noted that, for details not disclosed in the apparatus for predicting emission spectra of mixed fluorescent materials in the embodiments of the present invention, please refer to details disclosed in the method for predicting emission spectra of mixed fluorescent materials in the embodiments of the present invention, and details are not described here again.
In summary, the method is popularized to a luminescent material system mixed by multiple kinds of fluorescent powder by correcting the Bill model, training and learning of the neural network are carried out according to the proportionality coefficient obtained by the correction Bill model and the proportion of each fluorescent powder, the predicted value of the proportionality coefficient is obtained by applying a neural network algorithm, and then the predicted value is multiplied by the emission spectrum of the corresponding fluorescent powder and summed up, so that the prediction of the emission spectrum after the multiple kinds of fluorescent powder are mixed is realized, the redundant experimental test is replaced, the manpower and the time are saved, and the method has a certain guiding significance for researching the design of the high-quality white light LED light source.
In the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. The meaning of "a plurality of" is two or more, unless specifically defined otherwise.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily for the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps of the process, and further implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). In addition, the computer readable medium may even be paper or other suitable medium on which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like. While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (3)
1. The method for predicting the emission spectrum of the mixed fluorescent material is characterized by comprising the following steps of:
Respectively obtaining absorption spectrum, emission spectrum and quantum efficiency of N fluorescent materials, wherein N is an integer greater than or equal to 2;
obtaining the proportion of each fluorescent material mixed by the N fluorescent materials;
Calculating the ratio coefficients of the emission spectrums of the N fluorescent materials mixed according to any proportion;
Establishing a neural network prediction model according to the proportion of each fluorescent material and the emission spectrum proportion coefficient;
Acquiring an emission spectrum proportionality coefficient through the neural network prediction model, and predicting the emission spectrum of the mixed N fluorescent materials according to the emission spectrum proportionality coefficient acquired through the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient;
The calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion comprises the following steps:
calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion based on the modified Bell lambert model;
and establishing a neural network prediction model according to the proportion of each fluorescent material and the emission spectrum proportion coefficient, wherein the neural network prediction model comprises the following steps:
The method comprises the steps of taking the ratio of the emission spectrum of the N fluorescent materials mixed according to any proportion and the ratio of each fluorescent material as a data set of a neural network prediction model, and establishing the neural network prediction model, wherein the ratio of each fluorescent material is taken as an input variable of the neural network prediction model, and the ratio of the emission spectrum of the N fluorescent materials mixed according to any proportion is taken as an output variable;
According to the emission spectrum proportionality coefficient obtained by the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient, the method for predicting the emission spectrum after mixing N fluorescent materials comprises the following steps:
the emission spectrum proportional coefficients obtained through the neural network prediction model are multiplied by the emission spectrums of the corresponding fluorescent materials respectively, and then summed, so that the emission spectrums of the N fluorescent materials after being mixed can be calculated;
Calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion based on the modified Bill lambertian model, wherein the method comprises the following steps:
Calculating the absorption probability of mutual absorption among the fluorescent materials according to the absorption spectrum and the emission spectrum of each of the N fluorescent materials;
Calculating the ratio coefficient of the emission spectrum of the N fluorescent materials mixed according to any ratio according to the proportion of each fluorescent material, the quantum efficiency and the absorption probability of mutual absorption among the fluorescent materials;
The emission spectrum proportionality coefficient is generated by the following formula:
Wherein K i represents an emission spectrum proportionality coefficient, r i and r j represent proportions of fluorescent materials, q i and q j represent quantum efficiency of the fluorescent materials, δ ij represents probability that light emitted by the j-type fluorescent material is absorbed by the i-type fluorescent material, and δ ji represents probability that light emitted by the i-type fluorescent material is absorbed by the j-type fluorescent material;
The absorption probability of the mutual absorption between each fluorescent material is generated by the following formula:
Wherein ab i (λ) represents an i-type fluorescent material absorption spectrum, and em j (λ) represents a j-type fluorescent material emission spectrum.
2. The method of claim 1, wherein the predicted emission spectra of the N fluorescent materials after mixing is generated by the following formula:
Wherein em eq (λ) represents the predicted emission spectrum after mixing the N fluorescent materials, k i 'represents the emission spectrum proportionality coefficient obtained by the neural network prediction model, and em (λ) represents the emission spectrum of the fluorescent material corresponding to k i'.
3. A hybrid fluorescent material emission spectrum prediction device, comprising:
The first acquisition module is used for respectively acquiring the absorption spectrum, the emission spectrum and the quantum efficiency of N fluorescent materials, wherein N is an integer greater than or equal to 2;
the second acquisition module is used for acquiring the proportion of each fluorescent material after the N fluorescent materials are mixed;
the calculation module is used for calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion;
The building module is used for building a neural network prediction model according to the proportion of each fluorescent material and the emission spectrum proportion coefficient;
The prediction module is used for obtaining an emission spectrum proportionality coefficient through the neural network prediction model and predicting the emission spectrum of the mixed N fluorescent materials according to the emission spectrum proportionality coefficient obtained through the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient;
The calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion comprises the following steps:
calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion based on the modified Bell lambert model;
and establishing a neural network prediction model according to the proportion of each fluorescent material and the emission spectrum proportion coefficient, wherein the neural network prediction model comprises the following steps:
The method comprises the steps of taking the ratio of the emission spectrum of the N fluorescent materials mixed according to any proportion and the ratio of each fluorescent material as a data set of a neural network prediction model, and establishing the neural network prediction model, wherein the ratio of each fluorescent material is taken as an input variable of the neural network prediction model, and the ratio of the emission spectrum of the N fluorescent materials mixed according to any proportion is taken as an output variable;
According to the emission spectrum proportionality coefficient obtained by the neural network prediction model and the emission spectrum of the fluorescent material corresponding to the emission spectrum proportionality coefficient, the method for predicting the emission spectrum after mixing N fluorescent materials comprises the following steps:
the emission spectrum proportional coefficients obtained through the neural network prediction model are multiplied by the emission spectrums of the corresponding fluorescent materials respectively, and then summed, so that the emission spectrums of the N fluorescent materials after being mixed can be calculated;
Calculating the emission spectrum proportionality coefficient of the N fluorescent materials mixed according to any proportion based on the modified Bill lambertian model, wherein the method comprises the following steps:
Calculating the absorption probability of mutual absorption among the fluorescent materials according to the absorption spectrum and the emission spectrum of each of the N fluorescent materials;
Calculating the ratio coefficient of the emission spectrum of the N fluorescent materials mixed according to any ratio according to the proportion of each fluorescent material, the quantum efficiency and the absorption probability of mutual absorption among the fluorescent materials;
The emission spectrum proportionality coefficient is generated by the following formula:
Wherein K i represents an emission spectrum proportionality coefficient, r i and r j represent proportions of fluorescent materials, q i and q j represent quantum efficiency of the fluorescent materials, δ ij represents probability that light emitted by the j-type fluorescent material is absorbed by the i-type fluorescent material, and δ ji represents probability that light emitted by the i-type fluorescent material is absorbed by the j-type fluorescent material;
The absorption probability of the mutual absorption between each fluorescent material is generated by the following formula:
Wherein ab i (λ) represents an i-type fluorescent material absorption spectrum, and em j (λ) represents a j-type fluorescent material emission spectrum.
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