CN116296295A - Light path aging test method and system based on artificial intelligence - Google Patents
Light path aging test method and system based on artificial intelligence Download PDFInfo
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- CN116296295A CN116296295A CN202310380397.0A CN202310380397A CN116296295A CN 116296295 A CN116296295 A CN 116296295A CN 202310380397 A CN202310380397 A CN 202310380397A CN 116296295 A CN116296295 A CN 116296295A
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Abstract
The invention discloses an artificial intelligence-based light path aging test method and system, wherein the method comprises the following steps: obtaining aging data of different types of light paths under different test conditions; taking aging data of different types of light paths under different test conditions as training data samples to train an artificial intelligent model; acquiring the type of a current light path, and predicting the aging rule of the current type of light path through an artificial intelligent model; according to the predicted aging rule of the current type of light path, the test condition of the current type of light path is adjusted to accelerate the aging speed of the current type of light path; according to the invention, the ageing of different types of light paths is predicted by combining with the artificial intelligent model, so that the ageing rule of the current type of light path is obtained, and the test condition of the current type of light path is adjusted according to the ageing rule of the predicted current type of light path, so that the ageing speed of the current type of light path is accelerated, the ageing test time of a novel number of light paths is greatly reduced, and the research and development efficiency is greatly improved.
Description
Technical Field
The invention relates to the technical field of light path aging test, in particular to a light path aging test method and system based on artificial intelligence.
Background
With the development of society and the progress of science, laser is widely applied to various industries, and the existing fields using laser technology mainly include laser marking, laser welding, laser cutting, optical fiber communication, laser ranging, laser radar, laser weapon, laser record, laser vision correction, laser cosmetology, laser scanning, laser mosquito killer, LIF nondestructive testing technology and the like. Laser systems can be classified into continuous wave lasers and pulsed lasers. In the production of the laser industry, the light path aging test of a laser is crucial, and the essential steps are that the light path aging data directly influence the performance and quality of a product.
However, since the aging test process of the laser is complicated and takes a long time, and the updating of the laser is quick, the reliable aging test record of the newly developed laser cannot be completed quickly on the manufacturer, so that the marketing of the novel laser is delayed, and the research and development efficiency of the manufacturer is severely restricted.
Disclosure of Invention
The invention aims to provide an artificial intelligence-based light path aging test method and system, which can be used for carrying out aging prediction on different types of light paths by combining an artificial intelligence model so as to obtain the aging rule of the current type of light path, and adjusting the test condition of the current type of light path according to the aging rule of the predicted current type of light path so as to accelerate the aging speed of the current type of light path, thereby greatly reducing the aging test time of a novel type of light path and greatly improving the research and development efficiency.
In order to achieve the above purpose, the invention discloses an artificial intelligence-based optical path aging test method, which is suitable for aging prediction of an optical path, and the artificial intelligence-based optical path aging test method comprises the following steps:
s1, ageing data of different types of light paths under different test conditions are obtained, wherein the test conditions comprise ambient temperature, ambient humidity, visibility, input current, input voltage and test time, and the ageing data comprise illumination intensity and illumination temperature;
s2, taking aging data of different types of light paths under different test conditions as training data samples, and training an artificial intelligent model;
s3, obtaining the type of the current light path, and predicting the aging rule of the current type of light path through the artificial intelligent model;
s4, according to the predicted aging rule of the current type of light path, adjusting the test condition of the current type of light path to accelerate the aging speed of the current type of light path.
Preferably, the step S1 specifically includes:
s111, for the same type of optical path, measuring aging data of the current type of optical path by a single variable method, wherein the single variable method comprises the following steps of:
s112, taking any one of the ambient temperature, ambient humidity, visibility, input current, input voltage and test time of the test condition as a variable, and the rest is quantitative, adjusting the size of the variable with a preset step length every interval preset time, and recording aging data of the current type of light path;
s113, aging data of all types of light paths are measured through the single variable method.
Preferably, the step S1 specifically includes:
s121, for the same type of optical path, measuring aging data of the current type of optical path by a multivariate method, wherein the multivariate method comprises the following steps of:
s122, taking at least two of the ambient temperature, ambient humidity, visibility, input current, input voltage and test time of the test condition as variables, and the balance being quantitative, adjusting the size of the variables with a preset step length every interval preset time, and recording ageing data of the current type of light path;
s123, measuring ageing data of all types of light paths through the multivariate method.
Preferably, the step S2 specifically includes:
and taking different testing conditions of different types of light paths as input of an artificial intelligent model, taking ageing data of different types of light paths as output of the artificial intelligent model, and training the artificial intelligent model until the artificial intelligent model converges.
Preferably, the step S3 specifically includes:
s31, acquiring the type of a current light path;
s32, predicting aging data of the current type of light path under different test conditions through the artificial intelligent model;
s33, aging data of the current type of optical path under different test conditions are made into a visual chart so as to obtain the aging rule of the predicted current type of optical path.
Preferably, the method comprises the steps of, let the aging rate of the light path = a x illumination intensity + B x illumination temperature, wherein, a and B are constants, and the step S4 specifically includes:
s41, calculating and predicting corresponding aging rates under different test conditions in the aging rule of the current type of light path;
s42, analyzing corresponding test conditions when the current type of light path has the maximum aging rate;
s43, according to the corresponding test condition when the current type of optical path has the maximum aging rate, the test condition of the current type of optical path is adjusted so as to accelerate the aging speed of the current type of optical path.
Correspondingly, the invention also discloses an artificial intelligence-based light path aging test system, which comprises:
the first acquisition unit is used for acquiring ageing data of different types of light paths under different test conditions, wherein the test conditions comprise ambient temperature, ambient humidity, visibility, input current, input voltage and test time, and the ageing data comprise illumination intensity and illumination temperature;
the training unit is used for taking the aging data of different types of light paths under different test conditions as training data samples to train the artificial intelligent model;
the second acquisition unit is used for acquiring the type of the current optical path and predicting the aging rule of the optical path of the current type through the artificial intelligent model;
the adjusting unit is used for adjusting the test condition of the current type of light path according to the predicted aging rule of the current type of light path so as to accelerate the aging speed of the current type of light path.
Correspondingly, the invention also discloses a computer readable storage medium for storing a computer program, and the program is executed by a processor to realize the optical path aging test method based on artificial intelligence.
Compared with the prior art, the invention can combine the artificial intelligent model to predict the aging of different types of light paths to obtain the aging rule of the current type of light paths, and adjust the test condition of the current type of light paths according to the predicted aging rule of the current type of light paths to accelerate the aging speed of the current type of light paths, thereby greatly reducing the aging test time of the novel type of light paths and greatly improving the research and development efficiency.
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FIG. 1 is a flow chart of an artificial intelligence based foil defective detection method of the present invention;
fig. 2 is a block diagram of the foil membrane reject detection system based on artificial intelligence of the invention.
Detailed Description
In order to describe the technical content, the constructional features, the achieved objects and effects of the present invention in detail, the following description is made in connection with the embodiments and the accompanying drawings.
Referring to fig. 1, the optical path aging test method based on artificial intelligence of the present invention is suitable for performing aging prediction on an optical path, where the optical path includes, but is not limited to, an optical path system formed by lasers.
The optical path aging test method based on artificial intelligence comprises the following steps:
s1, ageing data of different types of light paths under different test conditions are obtained, wherein the test conditions comprise ambient temperature, ambient humidity, visibility, input current, input voltage and test time, and the ageing data comprise illumination intensity and illumination temperature.
S2, taking aging data of different types of light paths under different test conditions as training data samples, and training an artificial intelligent model.
S3, obtaining the type of the current light path, and predicting the aging rule of the light path of the current type through the artificial intelligent model.
S4, according to the predicted aging rule of the current type of light path, adjusting the test condition of the current type of light path to accelerate the aging speed of the current type of light path.
Preferably, the step S1 specifically includes:
s111, for the same type of optical path, measuring aging data of the current type of optical path by a single variable method, wherein the single variable method comprises the following steps.
And S112, taking any one of the ambient temperature, ambient humidity, visibility, input current, input voltage and test time of the test condition as a variable, and the rest is quantitative, adjusting the size of the variable with a preset step length every interval preset time, and recording ageing data of the current type of light path.
It will be appreciated that the single variable method herein may quantify the ambient temperature, the ambient humidity, the visibility, the input current, the input voltage, and the test time, and increase the ambient temperature by one unit every 10 minutes to achieve single variable adjustment of the test conditions.
S113, aging data of all types of light paths are measured through the single variable method.
Preferably, the step S1 specifically includes:
s121, for the same type of optical path, measuring aging data of the current type of optical path by a multivariate method, wherein the multivariate method comprises the following steps of:
s122, taking at least two of the ambient temperature, the ambient humidity, the visibility, the input current, the input voltage and the test time of the test condition as variables, and the rest is quantitative, adjusting the size of the variables by a preset step length every interval preset time, and recording the aging data of the current type of light path.
S123, measuring ageing data of all types of light paths through the multivariate method.
It will be appreciated that the multivariate method herein may quantify the visibility, input current, input voltage and test time with ambient temperature and ambient humidity as variables, increasing the ambient temperature and ambient humidity by one unit every 10 minutes to achieve a multivariate adjustment of the test conditions. Of course, three or four of the test conditions may be used as variables for adjustment, which will not be described herein.
Preferably, the step S2 specifically includes:
and taking different testing conditions of different types of light paths as input of an artificial intelligent model, taking ageing data of different types of light paths as output of the artificial intelligent model, and training the artificial intelligent model until the artificial intelligent model converges.
Preferably, the step S3 specifically includes:
s31, acquiring the type of a current light path;
s32, predicting aging data of the current type of light path under different test conditions through the artificial intelligent model;
s33, aging data of the current type of optical path under different test conditions are made into a visual chart so as to obtain the aging rule of the predicted current type of optical path.
Preferably, the method comprises the steps of, let the aging rate of the light path = a x illumination intensity + B x illumination temperature, wherein, a and B are constants, and the step S4 specifically includes:
s41, calculating and predicting corresponding aging rates under different test conditions in the aging rule of the current type of light path;
s42, analyzing corresponding test conditions when the current type of light path has the maximum aging rate;
s43, according to the corresponding test condition when the current type of optical path has the maximum aging rate, the test condition of the current type of optical path is adjusted so as to accelerate the aging speed of the current type of optical path.
It can be understood that the aging rate is the embodiment of the illumination intensity and the illumination temperature under different weights, and the weight ratio of the illumination intensity to the illumination temperature can be flexibly set according to the actual test requirement, so that the values of A and B are not limited.
Referring to fig. 2, correspondingly, the invention also discloses an artificial intelligence-based optical path aging test system, which comprises:
a first obtaining unit 10, configured to obtain ageing data of different types of optical paths under different test conditions, where the test conditions include an ambient temperature, an ambient humidity, a visibility, an input current, an input voltage, and a test time, and the ageing data includes an illumination intensity and an illumination temperature;
the training unit 20 is configured to train the artificial intelligence model by using the aging data of different types of optical paths under different test conditions as training data samples;
a second obtaining unit 30, configured to obtain a type of a current optical path, and predict an aging rule of the optical path of the current type through the artificial intelligence model;
the adjusting unit 40 is configured to adjust the test condition of the current type of optical path according to the predicted aging rule of the current type of optical path, so as to accelerate the aging speed of the current type of optical path.
Correspondingly, the invention also discloses a computer readable storage medium for storing a computer program, and the program is executed by a processor to realize the optical path aging test method based on artificial intelligence.
With reference to fig. 1 and fig. 2, the invention can combine the artificial intelligent model to predict the aging of different types of light paths to obtain the aging rule of the current type of light path, and adjust the test condition of the current type of light path according to the predicted aging rule of the current type of light path to accelerate the aging speed of the current type of light path, thereby greatly reducing the aging test time of the new type of light path and greatly improving the research and development efficiency.
The foregoing description of the preferred embodiments of the present invention is not intended to limit the scope of the claims, which follow, as defined in the claims.
Claims (8)
1. The optical path aging test method based on the artificial intelligence is suitable for carrying out aging prediction on an optical path and is characterized by comprising the following steps of:
obtaining ageing data of different types of light paths under different test conditions, wherein the test conditions comprise ambient temperature, ambient humidity, visibility, input current, input voltage and test time, and the ageing data comprise illumination intensity and illumination temperature;
taking aging data of different types of light paths under different test conditions as training data samples to train an artificial intelligent model;
acquiring the type of a current light path, and predicting the aging rule of the current type of light path through the artificial intelligent model;
according to the predicted aging rule of the current type of light path, the test condition of the current type of light path is adjusted to accelerate the aging speed of the current type of light path.
2. The artificial intelligence-based optical path aging test method according to claim 1, wherein the obtaining aging data of different types of optical paths under different test conditions specifically includes:
for the same type of optical path, the aging data of the current type of optical path is measured by a single variable method, wherein the single variable method comprises the following steps of:
taking any one of the environment temperature, the environment humidity, the visibility, the input current, the input voltage and the test time of the test condition as variables, and the rest is quantitative, adjusting the size of the variables with a preset step length every interval preset time, and recording the aging data of the current type of light path;
the aging data of all types of light paths are measured by the single variable method.
3. The artificial intelligence-based optical path aging test method according to claim 1, wherein the obtaining aging data of different types of optical paths under different test conditions specifically includes:
for the same type of optical path, aging data of the current type of optical path is measured by a multivariate method comprising the steps of:
taking at least two of the environment temperature, the environment humidity, the visibility, the input current, the input voltage and the test time of the test condition as variables, and the rest as quantification, adjusting the size of the variables with a preset step length every interval preset time, and recording the aging data of the current type of light path;
aging data of all types of optical paths are measured by the multivariate method.
4. The artificial intelligence based optical path aging test method according to claim 1, wherein the training of the artificial intelligence model using the aging data of the optical paths of different types under different test conditions as training data samples specifically comprises:
and taking different testing conditions of different types of light paths as input of an artificial intelligent model, taking ageing data of different types of light paths as output of the artificial intelligent model, and training the artificial intelligent model until the artificial intelligent model converges.
5. The method for testing the aging of the optical path based on the artificial intelligence according to claim 1, wherein the obtaining the type of the current optical path and predicting the aging rule of the optical path of the current type through the artificial intelligence model specifically comprises:
acquiring the type of a current light path;
predicting aging data of the current type of light path under different test conditions through the artificial intelligent model;
and (3) making aging data of the current type of light path into a visual chart under different test conditions so as to obtain the aging rule of the predicted current type of light path.
6. The artificial intelligence based optical path degradation testing method of claim 5, let the aging rate of the light path = a x illumination intensity + B x illumination temperature, where a and B are constants, according to the predicted aging rule of the current type of light path, the test condition of the current type of light path is adjusted to accelerate the aging speed of the current type of light path, and the method specifically comprises the following steps:
calculating and predicting corresponding aging rates under different test conditions in the aging rules of the current type of light path;
analyzing the corresponding test condition when the current type of light path has the maximum aging rate;
according to the corresponding test conditions when the current type of light path has the maximum aging rate, the test conditions of the current type of light path are adjusted so as to accelerate the aging speed of the current type of light path.
7. An artificial intelligence based optical path burn-in system, comprising:
the first acquisition unit is used for acquiring ageing data of different types of light paths under different test conditions, wherein the test conditions comprise ambient temperature, ambient humidity, visibility, input current, input voltage and test time, and the ageing data comprise illumination intensity and illumination temperature;
the training unit is used for taking the aging data of different types of light paths under different test conditions as training data samples to train the artificial intelligent model;
the second acquisition unit is used for acquiring the type of the current optical path and predicting the aging rule of the optical path of the current type through the artificial intelligent model;
the adjusting unit is used for adjusting the test condition of the current type of light path according to the predicted aging rule of the current type of light path so as to accelerate the aging speed of the current type of light path.
8. A computer-readable storage medium storing a computer program, characterized by: the program, when executed by a processor, implements the artificial intelligence based optical path burn-in test method of any one of claims 1 to 6.
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| CN115774179A (en) * | 2021-09-08 | 2023-03-10 | 长鑫存储技术有限公司 | Method and device for determining aging test conditions |
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Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN109756263A (en) * | 2018-12-20 | 2019-05-14 | 新华三大数据技术有限公司 | Fiber ageing prediction technique and device |
| CN115774179A (en) * | 2021-09-08 | 2023-03-10 | 长鑫存储技术有限公司 | Method and device for determining aging test conditions |
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