CN111222274A - Optical product model optimization method and system based on simplex method - Google Patents
Optical product model optimization method and system based on simplex method Download PDFInfo
- Publication number
- CN111222274A CN111222274A CN202010014124.0A CN202010014124A CN111222274A CN 111222274 A CN111222274 A CN 111222274A CN 202010014124 A CN202010014124 A CN 202010014124A CN 111222274 A CN111222274 A CN 111222274A
- Authority
- CN
- China
- Prior art keywords
- optical product
- optical
- simulation model
- module
- curvature
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Testing Of Optical Devices Or Fibers (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The optical product model optimization method and system based on the simplex method comprises the following steps: carrying out initialization on a plurality of groups of numerical values on the structural parameters of the simulation model; adding the thermal load into the simulation model to obtain temperature field distribution information; adding the structural load and the temperature field distribution information into the simulation model to obtain the displacement of each surface type; carrying out curvature fitting according to each profile displacement; carrying out optical analysis on the simulation model to obtain an imaging quality evaluation analysis result; substituting the displacement of each surface type, the fitted optical curvature and the imaging quality evaluation analysis result into an evaluation index cost function to obtain a performance evaluation index; selecting the optimal performance evaluation indexes from multiple groups; judging whether the optimal performance evaluation index meets the requirement, if so, discarding the structural parameter corresponding to the worst optical product performance evaluation index in the multiple groups of optical product performance evaluation indexes, and substituting the structural parameters of other groups into a simplex method for training to obtain an updated structural parameter; and updating the optical product simulation model based on the updated structural parameters.
Description
Technical Field
The invention relates to the field of industrial simulation, in particular to the field of optical-mechanical-thermal combined simulation, and particularly relates to a simplex method-based optical product model optimization method and system.
Background
In a complex environment, a temperature field of a typical optical-mechanical thermal coupling analysis can affect surface displacement and refractive index, and further affect the performance of an optical lens, and the surface displacement can be affected by structural load and the gravity load of the structure. In general, displacement, temperature and stress effects are finally fitted into a polynomial form and input into an optical surface type result. The traditional optical-mechanical thermal coupling analysis process can only evaluate the influence on an optical system under the working condition, cannot optimize the optical design, and can provide the optimization direction only by the manual experience of subject engineers such as structural and optical engineers.
The traditional design optimization is mainly carried out through the experience of engineers, whether the project design purpose is achieved depends on the abundance of the experience of the engineers, the engineers with abundant experience usually need to continuously use simulation software to carry out model parameter modification, result analysis, model modification again and result analysis again to find a satisfactory solution in the design optimization process, a large amount of repetitive work is carried out in the whole process, and the polynomial fitting has no substantial repair to the later-stage processing improvement.
Disclosure of Invention
Aiming at the problems and the defects in the prior art, the invention provides a novel optical product model optimization method and system based on a simplex method.
The invention solves the technical problems through the following technical scheme:
the invention provides an optical product model optimization method based on a simplex method, which is characterized by comprising the following steps:
s1, initializing a plurality of groups of numerical values of the structural parameters of the optical product simulation model of the optical product;
aiming at the optical product simulation model corresponding to each group of numerical structure parameters:
s2, loading the thermal load into the optical product simulation model, and analyzing the temperature field of the optical product simulation model to obtain temperature field distribution information;
s3, loading the structural load and the temperature field distribution information into the optical product simulation model, and analyzing the stress of the optical product simulation model to obtain the displacement of each surface type of the optical product simulation model;
s4, performing curvature fitting on the optical product simulation model according to the displacement of each surface type of the optical product simulation model to obtain the fitted optical curvature;
s5, carrying out optical analysis on the optical product simulation model to obtain an imaging quality evaluation analysis result;
s6, establishing an evaluation index cost function based on the displacement, optical curvature and imaging quality evaluation analysis result of each surface type of the optical product simulation model, and substituting the displacement of each surface type in the step S3, the fitted optical curvature in the step S4 and the imaging quality evaluation analysis result in the step S5 into the evaluation index cost function to obtain an optical product performance evaluation index;
s7, selecting the optimal optical product performance evaluation index from the optical product performance evaluation indexes corresponding to the obtained multiple groups of numerical value structure parameters;
s8, judging whether the optimal optical product performance evaluation index meets the preset requirement, if so, entering a step S11, otherwise, entering a step S9;
s9, discarding the structural parameters corresponding to the worst optical product performance evaluation indexes in the multiple groups of optical product performance evaluation indexes, and substituting the structural parameters of other groups into a simplex method to train and optimize to obtain updated structural parameters;
s10, updating the optical product simulation model based on the updated structure parameters, and executing the step S2 again;
and S11, ending the flow.
Preferably, in step S4, the equation of the surface type of the optical product simulation model is:
if the displacement dz exists, constructing a fitting error function f of the original surface type of the optical product simulation model and the fitted surface type after fitting:
wherein x is the x-axis coordinate of the surface type, y is the y-axis coordinate of the surface type, k is the coefficient of a quadratic standard surface, c0 is the original optical curvature corresponding to the original surface type, dz is the axial variation of the surface type in the z-axis direction, c1 is the fitted optical curvature, bzIs the axial translation in the z-axis direction of the fitted surface profile, w is the weight of the finite element mesh, S (c0, k, x)j,yj) As z-direction coordinate of the original surface, S (c1, k, x)j,yj) And (5) carrying out curvature fitting for the fitted curved surface z coordinate by using a factor iterative algorithm.
Preferably, the factor iteration algorithm is:
s41, initial natural coordinate c10、bz0，c10The initial value is selected as c0, bz0The initial value is selected to be 0, and β represents a high-order parameter;
s42, calculating c1 and bz according to the formula (3);
s43, judgment (c1-c 1)0)≤σ，(bz-bz0) Sigma is less than or equal to sigma, sigma represents a judgment parameter, if yes, c1 is used as the fitted optical curvature, otherwise, c10＝c1、bz0Steps S42 and S43 are repeatedly performed.
The invention also provides an optical product model optimization system based on the simplex method, which is characterized by comprising an initialization module, a first loading analysis module, a second loading analysis module, a curvature fitting module, an optical analysis module, an evaluation module, a selection module, a judgment module, a training module and an updating module;
the initialization module is used for initializing a plurality of groups of numerical values of the structural parameters of the optical product simulation model of the optical product;
aiming at the optical product simulation model corresponding to each group of numerical structure parameters:
the first loading analysis module is used for loading the heat load into the optical product simulation model and analyzing the temperature field of the optical product simulation model to obtain temperature field distribution information;
the second loading analysis module is used for loading the structural load and the temperature field distribution information into the optical product simulation model and analyzing the stress of the optical product simulation model so as to obtain the displacement of each surface type of the optical product simulation model;
the curvature fitting module is used for performing curvature fitting on the optical product simulation model according to the displacement of each surface type of the optical product simulation model to obtain the fitted optical curvature;
the optical analysis module is used for carrying out optical analysis on the optical product simulation model to obtain an imaging quality evaluation analysis result;
the evaluation module is used for establishing an evaluation index cost function based on the displacement, the optical curvature and the imaging quality evaluation analysis result of each surface type of the optical product simulation model, and substituting the displacement of each surface type in the second loading analysis module, the fitted optical curvature in the curvature fitting module and the imaging quality evaluation analysis result in the optical analysis module into the evaluation index cost function to obtain an optical product performance evaluation index;
the selection module is used for selecting the optimal optical product performance evaluation index from the optical product performance evaluation indexes corresponding to the obtained multiple groups of numerical value structure parameters;
the judging module is used for judging whether the optimal optical product performance evaluation index meets the preset requirement, if so, the judging module is ended, otherwise, the training module is called;
the training module is used for discarding the structural parameters corresponding to the worst optical product performance evaluation indexes in the multiple groups of optical product performance evaluation indexes, and substituting the structural parameters of other groups into a simplex method for training and optimizing to obtain updated structural parameters;
and the updating module is used for updating the optical product simulation model based on the updated structural parameter values and calling the first loading analysis module again.
Preferably, the equation of the surface type of the optical product simulation model is as follows:
if the displacement dz exists, constructing a fitting error function f of the original surface type of the optical product simulation model and the fitted surface type after fitting:
wherein x is the x-axis coordinate of the surface type, y is the y-axis coordinate of the surface type, k is the coefficient of a quadratic standard surface, c0 is the original optical curvature corresponding to the original surface type, dz is the axial variation of the surface type in the z-axis direction, c1 is the fitted optical curvature, bzIs the axial translation in the z-axis direction of the fitted surface profile, w is the weight of the finite element mesh, S (c0, k, x)j,yj) As z-direction coordinate of the original surface, S (c1, k, x)j,yj) And (5) carrying out curvature fitting for the fitted curved surface z coordinate by using a factor iterative algorithm.
Preferably, the factor iteration algorithm is:
initial natural coordinate c10、bz0，c10The initial value is selected as c0, bz0The initial value is selected to be 0, and β represents a high-order parameter;
c1, bz are calculated according to formula (3); judgment (c1-c 1)0)≤σ，(bz-bz0) Sigma is less than or equal to sigma, sigma represents a judgment parameter, if yes, c1 is used as the fitted optical curvature, otherwise, c10＝c1、bz0The calculation of c1 and bz is repeated until the judgment condition is satisfied.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows:
the invention introduces the curvature fitting and the simplex method into the optical-mechanical thermal coupling analysis, thereby solving the problem that the optical design optimization result can be used for the actual engineering manufacturing and processing, and further realizing the experimental manufacturing of the design analysis. The method can be directly used for the design optimization of optomechanical heat, and engineers do not rely on only manual experience for optimization any more.
Drawings
FIG. 1 is a flowchart of a simplex-based optical product model optimization method according to a preferred embodiment of the present invention.
FIG. 2 is a block diagram of a simplex-based optical product model optimization system according to a preferred embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, are within the scope of the present invention.
As shown in fig. 1, the present embodiment provides a simplex-based optical product model optimization method, which includes the following steps:
and S1, carrying out multi-group numerical value initialization on the structural parameters of the optical product simulation model of the optical product.
Aiming at the optical product simulation model corresponding to each group of numerical structure parameters:
and S2, loading the thermal load into the optical product simulation model, and analyzing the temperature field of the optical product simulation model to obtain temperature field distribution information.
And S3, loading the structural load and the temperature field distribution information into the optical product simulation model, and analyzing the stress of the optical product simulation model to obtain the displacement of each surface type of the optical product simulation model.
And S4, performing curvature fitting on the optical product simulation model according to the displacement of each surface type of the optical product simulation model to obtain the fitted optical curvature.
The equation of the surface form of the optical product simulation model is as follows:
if the displacement dz exists, constructing a fitting error function f of the original surface type of the optical product simulation model and the fitted surface type after fitting:
wherein x is the x-axis coordinate of the surface type, y is the y-axis coordinate of the surface type, k is the coefficient of a quadratic standard surface, c0 is the original optical curvature corresponding to the original surface type, dz is the axial variation of the surface type in the z-axis direction, c1 is the fitted optical curvature, bzIs the axial translation in the z-axis direction of the fitted surface profile, w is the weight of the finite element mesh, S (c0, k, x)j,yj) As z-direction coordinate of the original surface, S (c1, k, x)j,yj) And (5) carrying out curvature fitting for the fitted curved surface z coordinate by using a factor iterative algorithm.
The factor iteration algorithm is as follows:
s41, initial natural coordinate c10、bz0，c10The initial value is selected as c0, bz0The initial value is selected to be 0, and β represents a high-order parameter;
s42, calculating c1 and bz according to the formula (3);
s43, judgment (c1-c 1)0)≤σ，(bz-bz0) Sigma is less than or equal to sigma, sigma represents a judgment parameter, if all are finished, otherwise c10＝c1、bz0Steps S42 and S43 are repeatedly performed.
And S5, carrying out optical analysis on the optical product simulation model to obtain an imaging quality evaluation analysis result.
S6, establishing an evaluation index cost function based on the displacement, the optical curvature and the imaging quality evaluation analysis result of each surface type of the optical product simulation model, and substituting the displacement of each surface type in the step S3, the fitted optical curvature in the step S4 and the imaging quality evaluation analysis result in the step S5 into the evaluation index cost function to obtain the optical product performance evaluation index.
And S7, selecting the optimal optical product performance evaluation index from the optical product performance evaluation indexes corresponding to the obtained multiple groups of numerical value structure parameters.
And S8, judging whether the optimal optical product performance evaluation index meets the preset requirement, if so, entering the step S11, and otherwise, entering the step S9.
And S9, discarding the structural parameters corresponding to the worst optical product performance evaluation indexes in the multiple groups of optical product performance evaluation indexes, and substituting the structural parameters of other groups into a simplex method to train and optimize to obtain updated structural parameters.
S10, updating the optical product simulation model based on the updated structure parameters, and executing the step S2 again.
And S11, ending the flow.
As shown in fig. 2, the present invention further provides a simplex-based optical product model optimization system, which includes an initialization module 1, a first loading analysis module 2, a second loading analysis module 3, a curvature fitting module 4, an optical analysis module 5, an evaluation module 6, a selection module 7, a judgment module 8, a training module 9, and an update module 10.
The initialization module 1 is used for initializing a plurality of groups of numerical values of the structural parameters of the optical product simulation model of the optical product.
Aiming at the optical product simulation model corresponding to each group of numerical structure parameters:
the first loading analysis 2 module is used for loading the heat load into the optical product simulation model and analyzing the temperature field of the optical product simulation model to obtain temperature field distribution information.
The second loading analysis 3 module is used for loading the structural load and the temperature field distribution information into the optical product simulation model and analyzing the stress of the optical product simulation model so as to obtain the displacement of each surface type of the optical product simulation model.
And the curvature fitting module 4 is used for performing curvature fitting on the optical product simulation model according to the displacement of each surface type of the optical product simulation model to obtain the fitted optical curvature.
The equation of the surface form of the optical product simulation model is as follows:
if the displacement dz exists, constructing a fitting error function f of the original surface type of the optical product simulation model and the fitted surface type after fitting:
wherein x is the x-axis coordinate of the surface type, y is the y-axis coordinate of the surface type, k is the coefficient of a quadratic standard surface, c0 is the original optical curvature corresponding to the original surface type, dz is the axial variation of the surface type in the z-axis direction, c1 is the fitted optical curvature, bzIs the axial translation in the z-axis direction of the fitted surface profile, w is the weight of the finite element mesh, S (c0, k, x)j,yj) As z-direction coordinate of the original surface, S (c1, k, x)j,yj) And (5) carrying out curvature fitting for the fitted curved surface z coordinate by using a factor iterative algorithm.
The factor iteration algorithm is as follows:
initial natural coordinate c10、bz0，c10The initial value is selected as c0, bz0The initial value is selected to be 0, and β represents a high-order parameter;
c1, bz are calculated according to formula (3); judgment (c1-c 1)0)≤σ，(bz-bz0) Sigma is less than or equal to sigma, sigma represents a judgment parameter, if all are finished, otherwise c10＝c1、bz0The calculation of c1 and bz is repeated until the judgment condition is satisfied.
The optical analysis module 5 is used for carrying out optical analysis on the optical product simulation model to obtain an imaging quality evaluation analysis result.
The evaluation module 6 is configured to establish an evaluation index cost function based on the displacement, the optical curvature, and the imaging quality evaluation analysis result of each surface type of the optical product simulation model, and substitute the displacement of each surface type in the second loading analysis module, the fitted optical curvature in the curvature fitting module, and the imaging quality evaluation analysis result in the optical analysis module into the evaluation index cost function to obtain an optical product performance evaluation index.
The selection module 7 is used for selecting the optimal optical product performance evaluation index from the optical product performance evaluation indexes corresponding to the obtained multiple groups of numerical value structure parameters.
The judging module 8 is used for judging whether the optimal optical product performance evaluation index meets the preset requirement, if so, the operation is finished, otherwise, the training module 9 is called.
The training module 9 is configured to discard a structural parameter corresponding to a worst optical product performance evaluation index among the plurality of sets of optical product performance evaluation indexes, and substitute structural parameters of other sets into a simplex method to train and optimize to obtain an updated structural parameter;
the updating module 10 is configured to update the optical product simulation model based on the updated structural parameter value, and call the first loading analysis module again.
The optical product model optimization method and system based on the simplex method provided by the invention realize optimization design by introducing the simplex method on the basis of the optomechanical thermal process, and can be widely used for the optimization scheme selection at the initial stage of industrial product design.
In order to solve the problem of artificial experience optimization, the curvature fitting and the simplex method are introduced into the optical-mechanical thermal coupling analysis, so that the optical design optimization result can be used for actual engineering manufacturing and processing, and further the experimental manufacturing of design analysis is realized. The method can be directly used for the design optimization of optomechanical heat, and engineers do not rely on only manual experience for optimization any more.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.
Claims (6)
1. An optical product model optimization method based on a simplex method is characterized by comprising the following steps:
s1, initializing a plurality of groups of numerical values of the structural parameters of the optical product simulation model of the optical product;
aiming at the optical product simulation model corresponding to each group of numerical structure parameters:
s2, loading the thermal load into the optical product simulation model, and analyzing the temperature field of the optical product simulation model to obtain temperature field distribution information;
s3, loading the structural load and the temperature field distribution information into the optical product simulation model, and analyzing the stress of the optical product simulation model to obtain the displacement of each surface type of the optical product simulation model;
s4, performing curvature fitting on the optical product simulation model according to the displacement of each surface type of the optical product simulation model to obtain the fitted optical curvature;
s5, carrying out optical analysis on the optical product simulation model to obtain an imaging quality evaluation analysis result;
s6, establishing an evaluation index cost function based on the displacement, optical curvature and imaging quality evaluation analysis result of each surface type of the optical product simulation model, and substituting the displacement of each surface type in the step S3, the fitted optical curvature in the step S4 and the imaging quality evaluation analysis result in the step S5 into the evaluation index cost function to obtain an optical product performance evaluation index;
s7, selecting the optimal optical product performance evaluation index from the optical product performance evaluation indexes corresponding to the obtained multiple groups of numerical value structure parameters;
s8, judging whether the optimal optical product performance evaluation index meets the preset requirement, if so, entering a step S11, otherwise, entering a step S9;
s9, discarding the structural parameters corresponding to the worst optical product performance evaluation indexes in the multiple groups of optical product performance evaluation indexes, and substituting the structural parameters of other groups into a simplex method to train and optimize to obtain updated structural parameters;
s10, updating the optical product simulation model based on the updated structure parameters, and executing the step S2 again;
and S11, ending the flow.
2. The simplex-based optical product model optimization method of claim 1, wherein in step S4, the equation of the surface type of the optical product simulation model is:
if the displacement dz exists, constructing a fitting error function f of the original surface type of the optical product simulation model and the fitted surface type after fitting:
wherein x is the x-axis coordinate of the surface type, y is the y-axis coordinate of the surface type, k is the coefficient of a quadratic standard surface, c0 is the original optical curvature corresponding to the original surface type, dz is the axial variation of the surface type in the z-axis direction, c1 is the fitted optical curvature, bzIs the axial translation in the z-axis direction of the fitted surface profile, w is the weight of the finite element mesh, S (c0, k, x)j,yj) As z-direction coordinate of the original surface, S (c1, k, x)j,yj) And (5) carrying out curvature fitting for the fitted curved surface z coordinate by using a factor iterative algorithm.
3. The simplex-based optical product model optimization method of claim 1, wherein the factorial iterative algorithm is:
s41, initial natural coordinate c10、bz0，c10The initial value is selected as c0, bz0The initial value is selected to be 0, and β represents a high-order parameter;
s42, calculating c1 and bz according to the formula (3);
s43, judgment (c1-c 1)0)≤σ，(bz-bz0) Sigma is less than or equal to sigma, sigma represents a judgment parameter, if yes, c1 is used as the fitted optical curvature, otherwise, c10＝c1、bz0Steps S42 and S43 are repeatedly performed.
4. An optical product model optimization system based on a simplex method is characterized by comprising an initialization module, a first loading analysis module, a second loading analysis module, a curvature fitting module, an optical analysis module, an evaluation module, a selection module, a judgment module, a training module and an updating module;
the initialization module is used for initializing a plurality of groups of numerical values of the structural parameters of the optical product simulation model of the optical product;
aiming at the optical product simulation model corresponding to each group of numerical structure parameters:
the first loading analysis module is used for loading the heat load into the optical product simulation model and analyzing the temperature field of the optical product simulation model to obtain temperature field distribution information;
the second loading analysis module is used for loading the structural load and the temperature field distribution information into the optical product simulation model and analyzing the stress of the optical product simulation model so as to obtain the displacement of each surface type of the optical product simulation model;
the curvature fitting module is used for performing curvature fitting on the optical product simulation model according to the displacement of each surface type of the optical product simulation model to obtain the fitted optical curvature;
the optical analysis module is used for carrying out optical analysis on the optical product simulation model to obtain an imaging quality evaluation analysis result;
the evaluation module is used for establishing an evaluation index cost function based on the displacement, the optical curvature and the imaging quality evaluation analysis result of each surface type of the optical product simulation model, and substituting the displacement of each surface type in the second loading analysis module, the fitted optical curvature in the curvature fitting module and the imaging quality evaluation analysis result in the optical analysis module into the evaluation index cost function to obtain an optical product performance evaluation index;
the selection module is used for selecting the optimal optical product performance evaluation index from the optical product performance evaluation indexes corresponding to the obtained multiple groups of numerical value structure parameters;
the judging module is used for judging whether the optimal optical product performance evaluation index meets the preset requirement, if so, the judgment is finished, otherwise, the coding module is called;
the training module is used for discarding the structural parameters corresponding to the worst optical product performance evaluation indexes in the multiple groups of optical product performance evaluation indexes, and substituting the structural parameters of other groups into a simplex method for training and optimizing to obtain updated structural parameters;
and the updating module is used for updating the optical product simulation model based on the updated structural parameter values and calling the first loading analysis module again.
5. The simplex-based optical product model optimization system of claim 4, wherein the equation for the surface form of the optical product simulation model is:
if the displacement dz exists, constructing a fitting error function f of the original surface type of the optical product simulation model and the fitted surface type after fitting:
wherein x is the x-axis coordinate of the surface type, and y is the surface typeK is a quadratic standard surface coefficient, c0 is an original optical curvature corresponding to an original surface type, dz is an axial variation of the surface type in the z-axis direction, c1 is an optical curvature after fitting, bzIs the axial translation in the z-axis direction of the fitted surface profile, w is the weight of the finite element mesh, S (c0, k, x)j,yj) As z-direction coordinate of the original surface, S (c1, k, x)j,yj) And (5) carrying out curvature fitting for the fitted curved surface z coordinate by using a factor iterative algorithm.
6. The simplex-based optical product model optimization system of claim 4, wherein the factorial iterative algorithm is:
initial natural coordinate c10、bz0，c10The initial value is selected as c0, bz0The initial value is selected to be 0, and β represents a high-order parameter;
c1, bz are calculated according to formula (3); judgment (c1-c 1)0)≤σ，(bz-bz0) Sigma is less than or equal to sigma, sigma represents a judgment parameter, if yes, c1 is used as the fitted optical curvature, otherwise, c10＝c1、bz0The calculation of c1 and bz is repeated until the judgment condition is satisfied.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010014124.0A CN111222274B (en) | 2020-01-07 | 2020-01-07 | Optical product model optimization method and system based on simplex method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010014124.0A CN111222274B (en) | 2020-01-07 | 2020-01-07 | Optical product model optimization method and system based on simplex method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN111222274A true CN111222274A (en) | 2020-06-02 |
| CN111222274B CN111222274B (en) | 2021-06-25 |
Family
ID=70832265
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010014124.0A Active CN111222274B (en) | 2020-01-07 | 2020-01-07 | Optical product model optimization method and system based on simplex method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111222274B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113995552A (en) * | 2020-07-27 | 2022-02-01 | 河南赛美视生物科技有限公司 | Method for manufacturing artificial lens for precise surface shape control and artificial lens |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1898590A (en) * | 2003-12-01 | 2007-01-17 | Cdm光学有限公司 | System and method for optimizing optical and digital system designs |
| US20070038417A1 (en) * | 2005-07-28 | 2007-02-15 | Taiwan Semiconductor Manufacturing Company, Ltd. | Regression system and methods for optical proximity correction modeling |
| CN105321169A (en) * | 2014-05-31 | 2016-02-10 | 苹果公司 | Method and system for a single frame camera module active alignment tilt correction |
| CN107391925A (en) * | 2017-07-19 | 2017-11-24 | 长春理工大学 | Optical thin film characterizing method based on cloud model quantum evolutionary algorithm |
-
2020
- 2020-01-07 CN CN202010014124.0A patent/CN111222274B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1898590A (en) * | 2003-12-01 | 2007-01-17 | Cdm光学有限公司 | System and method for optimizing optical and digital system designs |
| US20070038417A1 (en) * | 2005-07-28 | 2007-02-15 | Taiwan Semiconductor Manufacturing Company, Ltd. | Regression system and methods for optical proximity correction modeling |
| CN105321169A (en) * | 2014-05-31 | 2016-02-10 | 苹果公司 | Method and system for a single frame camera module active alignment tilt correction |
| CN107391925A (en) * | 2017-07-19 | 2017-11-24 | 长春理工大学 | Optical thin film characterizing method based on cloud model quantum evolutionary algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 沈伟东，等: "确定薄膜厚度和光学常数的一种新方法", 《光学学报》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113995552A (en) * | 2020-07-27 | 2022-02-01 | 河南赛美视生物科技有限公司 | Method for manufacturing artificial lens for precise surface shape control and artificial lens |
Also Published As
| Publication number | Publication date |
|---|---|
| CN111222274B (en) | 2021-06-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111274673B (en) | Optical product model optimization method and system based on particle swarm optimization | |
| CN112987664B (en) | A flow shop scheduling method based on deep reinforcement learning | |
| CN113516133A (en) | Multi-modal image classification method and system | |
| CN111241669B (en) | Genetic optimization algorithm-based optical product model optimization method and system | |
| CN111597631B (en) | Optimization method of vehicle wind resistance coefficient based on adaptive surrogate model | |
| CN114492895A (en) | Batching and scheduling method for flexible production line of automobile engine | |
| CN111222274B (en) | Optical product model optimization method and system based on simplex method | |
| CN113590748B (en) | Emotion classification continuous learning method based on iterative network combination and storage medium | |
| CN111553118A (en) | Multidimensional continuous optimization variable global optimization method based on reinforcement learning | |
| CN116309022A (en) | Ancient architecture image self-adaptive style migration method based on visual encoder | |
| CN115310227A (en) | Method for constructing gear power propagation digital twin model | |
| CN114154847B (en) | Method and device for determining engineering construction scheme, client and storage medium | |
| CN119476016A (en) | Mechanical structure reliability analysis method and system based on active learning agent model | |
| KR102660361B1 (en) | Sequential Embedding Statistical Analysis for Multidimensional Tolerance Limits | |
| CN117992702A (en) | Depth-generic-parameter-depth operator network method for solving parameter-containing ODEs (ordinary differential equation) | |
| CN113126583A (en) | Real-time interactive control method and system for digital twin spinning complete equipment | |
| CN117994122A (en) | Image style migration method based on cyclic generation countermeasure network | |
| CN114722658A (en) | Application method of data-driven multi-trust degree cooperative meta model in prediction of welding deformation of fuel tank | |
| CN115841190A (en) | Road PCI multi-step prediction method and device | |
| CN115562202A (en) | An Intelligent Planning Algorithm for Assembly Sequence | |
| CN113626827A (en) | Intelligent contract vulnerability detection method, system, equipment, medium and terminal | |
| CN113988674A (en) | An intelligent production scheduling method for manufacturing based on deep learning | |
| CN113191978A (en) | Asynchronous implementation method of distributed constraint edge-varying FIR (finite impulse response) graph filter | |
| CN118709724B (en) | Reduced manifold neural operator construction method for space-time process unequal domain mapping | |
| CN115310228B (en) | Gear modification design method based on digital twin |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Address after: 201206 Shanghai, Pudong New Area, China (Shanghai) free trade zone, new Jinqiao Road, No. 13, building 2, floor 27 Applicant after: Shanghai suochen Information Technology Co.,Ltd. Address before: 201204 No. 27, Lane 676, Wuxing Road, Pudong New Area, Shanghai Applicant before: SHANGHAI SUOCHEN INFORMATION TECHNOLOGY Co.,Ltd. |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20250623 Address after: 201204 1st Floor, No.27, Lane 676, Wuxing Road, Pudong New Area, Shanghai Patentee after: Shanghai Suotan Software Technology Co.,Ltd. Country or region after: China Address before: 201206 Shanghai, Pudong New Area, China (Shanghai) free trade zone, new Jinqiao Road, No. 13, building 2, floor 27 Patentee before: Shanghai suochen Information Technology Co.,Ltd. Country or region before: China |