CN114419034A - Automatic detection method, system and medium for intelligent wearable silica gel material - Google Patents
Automatic detection method, system and medium for intelligent wearable silica gel material Download PDFInfo
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- VYPSYNLAJGMNEJ-UHFFFAOYSA-N Silicium dioxide Chemical compound O=[Si]=O VYPSYNLAJGMNEJ-UHFFFAOYSA-N 0.000 title claims abstract description 420
- 239000000463 material Substances 0.000 title claims abstract description 406
- 239000000741 silica gel Substances 0.000 title claims abstract description 376
- 229910002027 silica gel Inorganic materials 0.000 title claims abstract description 376
- 238000001514 detection method Methods 0.000 title claims abstract description 119
- 230000007547 defect Effects 0.000 claims abstract description 243
- 230000003595 spectral effect Effects 0.000 claims abstract description 78
- 230000008859 change Effects 0.000 claims abstract description 54
- 230000017525 heat dissipation Effects 0.000 claims abstract description 42
- 238000000034 method Methods 0.000 claims abstract description 41
- 238000012545 processing Methods 0.000 claims abstract description 31
- 239000000377 silicon dioxide Substances 0.000 claims abstract description 22
- 238000007781 pre-processing Methods 0.000 claims abstract description 19
- 238000012937 correction Methods 0.000 claims description 49
- 238000012549 training Methods 0.000 claims description 30
- 238000005452 bending Methods 0.000 claims description 15
- 230000008569 process Effects 0.000 claims description 13
- 238000012795 verification Methods 0.000 claims description 12
- 238000012360 testing method Methods 0.000 claims description 11
- 238000011156 evaluation Methods 0.000 claims description 10
- 230000004044 response Effects 0.000 claims description 10
- 238000001228 spectrum Methods 0.000 claims description 7
- 238000013528 artificial neural network Methods 0.000 claims description 6
- 238000012544 monitoring process Methods 0.000 claims description 6
- 238000003709 image segmentation Methods 0.000 claims description 5
- 229920001296 polysiloxane Polymers 0.000 claims description 5
- 238000007689 inspection Methods 0.000 description 4
- 230000008878 coupling Effects 0.000 description 3
- 238000010168 coupling process Methods 0.000 description 3
- 238000005859 coupling reaction Methods 0.000 description 3
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- 230000018109 developmental process Effects 0.000 description 1
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- 230000006872 improvement Effects 0.000 description 1
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Abstract
The invention discloses an automatic detection method, a system and a medium for intelligent wearable silica materials, which comprise the following steps: the method comprises the steps of obtaining hyperspectral image information of a silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the silica gel material; constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics; classifying the silica gel material containing the defects according to the defect types, and judging whether the silica gel material containing the defects can be subjected to secondary processing; meanwhile, the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material. The invention carries out defect detection and heat dissipation reliability detection through the silica gel material for intelligent wearing, thereby improving the detection efficiency and reducing the rejection rate of the silica gel material.
Description
Technical Field
The invention relates to the technical field of silica gel detection, in particular to an automatic detection method, system and storage medium for intelligent wearable silica gel materials.
Background
With the development of science and technology and the continuous improvement of manufacturing processes, intelligent wearable devices (particularly intelligent watches and wristbands) are more and more popular in daily life, can record daily activities of people, and provide a new way for receiving information for people.
In order to carry out more comprehensive automatic detection on the silica gel material for intelligent wearing, a system needs to be developed to be matched with the silica gel material for realization, and the system acquires hyperspectral image information of the silica gel material, carries out pretreatment and acquires spectral characteristics of the silica gel material; constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material according to the spectral characteristics; classifying the silica gel material containing the defects according to the defect types, and judging whether the silica gel material containing the defects can be subjected to secondary processing; meanwhile, the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material. How to detect the defects and the reliability of the silica gel material in the implementation process of the system is a problem which needs to be solved urgently.
Disclosure of Invention
In order to solve the technical problem, the invention provides an automatic detection method and system for intelligent wearable silica materials and a storage medium.
The invention provides an automatic detection method of a silica material for intelligent wearing, which comprises the following steps:
the method comprises the steps of obtaining hyperspectral image information of a target silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the target silica gel material;
constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics;
classifying the target silica gel material containing the defects according to the defect types, and judging whether the target silica gel material containing the defects can be subjected to secondary processing or not;
meanwhile, the thickness information of the target silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the target silica gel material is corrected according to the actual temperature change of the target silica gel material.
In this scheme, the preprocessing is performed on the hyperspectral image information to obtain the spectral characteristics of the target silica gel material, and the method specifically comprises the following steps:
acquiring hyperspectral image information of a target silica gel material, performing black-and-white correction processing on the hyperspectral image information to perform denoising, acquiring an interested area in the hyperspectral image information through image segmentation, extracting spectral data of the interested area,
processing the spectral data by a continuous projection method, eliminating the collinearity among original data variables, and extracting an optimal response spectral band;
and extracting spectral characteristics according to the spectral data of the optimal response spectrum band.
In this scheme, the silica gel material defect detection model is constructed, and the surface defect and the internal defect of the target silica gel material are obtained through the silica gel material defect detection model according to the spectral characteristics, specifically:
establishing a neural network, establishing a silica gel material defect detection model, acquiring massive silica gel material spectral data containing internal defects and internal defects through big data, preprocessing and extracting spectral characteristics to generate a training set and a verification set;
importing the training set into a silica gel material defect detection model for iterative training, and adjusting relevant parameters of the silica gel material defect detection model according to the iterative training;
presetting an error threshold of a silica gel material defect detection model, and calculating the error of the silica gel material defect detection model after multiple iterative training;
when the error is smaller than a preset error threshold value, performing a comparison test on the output result and the verification set, and when the deviation value is smaller than a preset deviation, obtaining a trained silica gel material defect detection model;
and introducing the spectral characteristics of the target silica gel material into the trained silica gel material defect detection model, and analyzing the spectral characteristics through the silica gel material defect detection model to generate defect information of the silica gel sample.
In this scheme, the step of classifying the target silica gel material containing the defects according to the defect types and judging whether the target silica gel material containing the defects can be subjected to secondary processing specifically comprises the following steps:
classifying the target silica gel material containing the defects according to the internal defects and the surface defects to obtain defect characteristics, and matching the characteristic information with the target silica gel material containing the defects;
acquiring position characteristics and size characteristics of the defects through the defect characteristics of a target silica gel material containing the surface defects, and evaluating the surface defects according to the position characteristics and the size characteristics according to a preset evaluation standard;
judging whether the evaluation score of the surface defect is larger than a preset score threshold value or not, if so, judging the target silica gel material containing the surface defect as an unqualified product, and if not, performing secondary processing on the target silica gel material containing the surface defect;
and judging the target silica gel material containing the internal defects as an unqualified product.
In this scheme, the thickness information of silica gel material is confirmed to the heat dissipation demand according to intelligent wearing equipment to the thickness information of silica gel material is revised according to the actual temperature change of silica gel material, specifically is:
acquiring the highest operating temperature of an intelligent terminal in the intelligent wearable device and the information of the wrapping area of a target silica gel material on the intelligent terminal;
obtaining the material heat conduction characteristic of a target silica gel material, and determining the thickness information of the silica gel material by combining the highest operation temperature and the wrapping area information with the material heat conduction characteristic;
acquiring initial temperature information of a silica gel material in the operation process of the intelligent wearable device, monitoring a target silica gel material in the operation process, and acquiring real-time temperature information of the silica gel material;
when the real-time temperature information reaches the highest temperature information, extracting time information used for temperature change, and calculating the temperature change rate of the target silica gel material according to the time information and the temperature difference value between the highest temperature information and the initial temperature information;
presetting a temperature change rate threshold, judging whether the temperature change rate is greater than the temperature change rate threshold, and if so, proving that the heat dissipation capacity of the intelligent wearable equipment does not reach a preset standard;
meanwhile, correction information is generated, and the thickness of the target silica gel material in the intelligent wearable device is corrected through the correction information.
In this scheme, still include:
performing bending reliability test on the target silica gel material after the thickness correction is finished, and acquiring the maximum bending frequency information of the target silica gel material after the thickness correction is finished;
comparing and judging the maximum bending times information with the original reliability, and calculating the reliability deviation rate before and after thickness correction;
judging whether the reliability deviation rate is within a preset range, and if so, taking the thickness information of the target silica gel material after the thickness correction as the preset thickness of the target silica gel material on the intelligent wearable device;
if the target silica gel material is not in the position, the reliability of the target silica gel material after the thickness correction cannot meet the reliability standard, the target silica gel material is corrected again to generate the parcel area correction information, and the heat dissipation requirement of the intelligent terminal is met by correcting the contact area of the target silica gel material.
The second aspect of the present invention also provides an automatic detection system for intelligent wearable silica materials, comprising: the automatic detection method program for the intelligent wearable silica material comprises the following steps when executed by the processor:
the method comprises the steps of obtaining hyperspectral image information of a target silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the target silica gel material;
constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics;
classifying the target silica gel material containing the defects according to the defect types, and judging whether the target silica gel material containing the defects can be subjected to secondary processing or not;
meanwhile, the thickness information of the target silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the target silica gel material is corrected according to the actual temperature change of the target silica gel material.
In this scheme, the preprocessing is performed on the hyperspectral image information to obtain the spectral characteristics of the target silica gel material, and the method specifically comprises the following steps:
acquiring hyperspectral image information of a target silica gel material, performing black-and-white correction processing on the hyperspectral image information to perform denoising, acquiring an interested area in the hyperspectral image information through image segmentation, extracting spectral data of the interested area,
processing the spectral data by a continuous projection method, eliminating the collinearity among original data variables, and extracting an optimal response spectral band;
and extracting spectral characteristics according to the spectral data of the optimal response spectrum band.
In this scheme, the silica gel material defect detection model is constructed, and the surface defect and the internal defect of the target silica gel material are obtained through the silica gel material defect detection model according to the spectral characteristics, specifically:
establishing a neural network, establishing a silica gel material defect detection model, acquiring massive silica gel material spectral data containing internal defects and internal defects through big data, preprocessing and extracting spectral characteristics to generate a training set and a verification set;
importing the training set into a silica gel material defect detection model for iterative training, and adjusting relevant parameters of the silica gel material defect detection model according to the iterative training;
presetting an error threshold of a silica gel material defect detection model, and calculating the error of the silica gel material defect detection model after multiple iterative training;
when the error is smaller than a preset error threshold value, performing a comparison test on the output result and the verification set, and when the deviation value is smaller than a preset deviation, obtaining a trained silica gel material defect detection model;
and introducing the spectral characteristics of the target silica gel material into the trained silica gel material defect detection model, and analyzing the spectral characteristics through the silica gel material defect detection model to generate defect information of the silica gel sample.
In this scheme, the step of classifying the target silica gel material containing the defects according to the defect types and judging whether the target silica gel material containing the defects can be subjected to secondary processing specifically comprises the following steps:
classifying the target silica gel material containing the defects according to the internal defects and the surface defects to obtain defect characteristics, and matching the characteristic information with the target silica gel material containing the defects;
acquiring position characteristics and size characteristics of the defects through the defect characteristics of a target silica gel material containing the surface defects, and evaluating the surface defects according to the position characteristics and the size characteristics according to a preset evaluation standard;
judging whether the evaluation score of the surface defect is larger than a preset score threshold value or not, if so, judging the target silica gel material containing the surface defect as an unqualified product, and if not, performing secondary processing on the target silica gel material containing the surface defect;
and judging the target silica gel material containing the internal defects as an unqualified product.
In this scheme, the thickness information of silica gel material is confirmed to the heat dissipation demand according to intelligent wearing equipment to the thickness information of silica gel material is revised according to the actual temperature change of silica gel material, specifically is:
acquiring the highest operating temperature of an intelligent terminal in the intelligent wearable device and the information of the wrapping area of a target silica gel material on the intelligent terminal;
obtaining the material heat conduction characteristic of a target silica gel material, and determining the thickness information of the silica gel material by combining the highest operation temperature and the wrapping area information with the material heat conduction characteristic;
acquiring initial temperature information of a silica gel material in the operation process of the intelligent wearable device, monitoring a target silica gel material in the operation process, and acquiring real-time temperature information of the silica gel material;
when the real-time temperature information reaches the highest temperature information, extracting time information used for temperature change, and calculating the temperature change rate of the target silica gel material according to the time information and the temperature difference value between the highest temperature information and the initial temperature information;
presetting a temperature change rate threshold, judging whether the temperature change rate is greater than the temperature change rate threshold, and if so, proving that the heat dissipation capacity of the intelligent wearable equipment does not reach a preset standard;
meanwhile, correction information is generated, and the thickness of the target silica gel material in the intelligent wearable device is corrected through the correction information.
In this scheme, still include:
performing bending reliability test on the target silica gel material after the thickness correction is finished, and acquiring the maximum bending frequency information of the target silica gel material after the thickness correction is finished;
comparing and judging the maximum bending times information with the original reliability, and calculating the reliability deviation rate before and after thickness correction;
judging whether the reliability deviation rate is within a preset range, and if so, taking the thickness information of the target silica gel material after the thickness correction as the preset thickness of the target silica gel material on the intelligent wearable device;
if the target silica gel material is not in the position, the reliability of the target silica gel material after the thickness correction cannot meet the reliability standard, the target silica gel material is corrected again to generate the parcel area correction information, and the heat dissipation requirement of the intelligent terminal is met by correcting the contact area of the target silica gel material.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an automatic detection method for a smart wearable silica material, and when the program of the automatic detection method for a smart wearable silica material is executed by a processor, the steps of the method for automatically detecting a smart wearable silica material as described in any one of the above are implemented.
The invention discloses an automatic detection method, a system and a storage medium for intelligent wearable silica materials, wherein the method comprises the following steps: the method comprises the steps of obtaining hyperspectral image information of a silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the silica gel material; constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics; classifying the silica gel material containing the defects according to the defect types, and judging whether the silica gel material containing the defects can be subjected to secondary processing; meanwhile, the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material. The invention carries out defect detection and heat dissipation reliability detection through the silica gel material for intelligent wearing, thereby improving the detection efficiency and reducing the rejection rate of the silica gel material.
Drawings
FIG. 1 illustrates a flow chart of a method for automated detection of smart wearable silicone materials in accordance with the present invention;
FIG. 2 is a flow chart illustrating the detection of the thickness of a target silica gel material according to the heat dissipation requirement according to the present invention;
fig. 3 shows a block diagram of an automated detection system for smart wearable silicone materials in accordance with the present invention.
Detailed Description
In order that the above objects, features and advantages of the present invention can be more clearly understood, a more particular description of the invention will be rendered by reference to the appended drawings. It should be noted that the embodiments and features of the embodiments of the present application may be combined with each other without conflict.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, however, the present invention may be practiced in other ways than those specifically described herein, and therefore the scope of the present invention is not limited by the specific embodiments disclosed below.
Fig. 1 shows a flow chart of an automated detection method of a silica material for smart wear according to the present invention.
As shown in fig. 1, a first aspect of the present invention provides an automatic detection method for a silica material for smart wearing, including:
s102, acquiring hyperspectral image information of a target silica gel material, preprocessing the hyperspectral image information, and acquiring spectral characteristics of the target silica gel material;
s104, constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics;
s106, classifying the target silica gel material containing the defects according to the defect types, and judging whether the target silica gel material containing the defects can be subjected to secondary processing or not;
and S108, determining the thickness information of the target silica gel material according to the heat dissipation requirement of the intelligent wearable device, and correcting the thickness information of the target silica gel material according to the actual temperature change of the target silica gel material.
It should be noted that, the preprocessing is performed on the hyperspectral image information to obtain the spectral characteristics of the target silica gel material, and specifically the method comprises the following steps: acquiring hyperspectral image information of a target silica gel material, performing black-and-white correction processing on the hyperspectral image information to remove noise, acquiring an interested area in the hyperspectral image information through image segmentation, extracting spectral data of the interested area, processing the spectral data through a continuous projection method, eliminating collinearity among original data variables, and extracting an optimal response spectrum section; and extracting spectral characteristics according to the spectral data of the optimal response spectrum band.
It should be noted that, the constructing of the silica gel material defect detection model, and obtaining the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics specifically include:
establishing a neural network, establishing a silica gel material defect detection model, acquiring massive silica gel material spectral data containing internal defects and internal defects through big data, preprocessing and extracting spectral characteristics to generate a training set and a verification set; importing the training set into a silica gel material defect detection model for iterative training, and adjusting relevant parameters of the silica gel material defect detection model according to the iterative training; presetting an error threshold of a silica gel material defect detection model, and calculating the error of the silica gel material defect detection model after multiple iterative training; when the error is smaller than a preset error threshold value, performing a comparison test on the output result and the verification set, and when the deviation value is smaller than a preset deviation, obtaining a trained silica gel material defect detection model; and introducing the spectral characteristics of the target silica gel material into the trained silica gel material defect detection model, and analyzing the spectral characteristics through the silica gel material defect detection model to generate defect information of the silica gel sample.
It should be noted that, the classifying the target silica gel material containing the defect according to the defect type and determining whether the target silica gel material containing the defect can be subjected to the secondary processing specifically include:
classifying the target silica gel material containing the defects according to the internal defects and the surface defects to obtain defect characteristics, and matching the characteristic information with the target silica gel material containing the defects; acquiring position characteristics and size characteristics of the defects through the defect characteristics of a target silica gel material containing the surface defects, and evaluating the surface defects according to the position characteristics and the size characteristics according to a preset evaluation standard; judging whether the evaluation score of the surface defect is larger than a preset score threshold value or not, if so, judging the target silica gel material containing the surface defect as an unqualified product, and if not, performing secondary processing on the target silica gel material containing the surface defect; and judging the target silica gel material containing the internal defects as an unqualified product.
Fig. 2 shows a flow chart of detecting the thickness of a target silica gel material according to the heat dissipation requirement in the present invention.
According to the embodiment of the invention, the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material, specifically:
s202, acquiring the highest operating temperature of an intelligent terminal in the intelligent wearable device and information of the wrapping area of the intelligent terminal by a target silica gel material;
s204, obtaining the material heat conduction characteristic of the target silica gel material, and determining the thickness information of the silica gel material by combining the highest operation temperature and the wrapping area information with the material heat conduction characteristic;
s206, acquiring initial temperature information of the silica gel material in the operation process of the intelligent wearable device, monitoring a target silica gel material in the operation process, and acquiring real-time temperature information of the silica gel material;
s208, when the real-time temperature information reaches the highest temperature information, extracting time information for temperature change, and calculating the temperature change rate of the target silica gel material according to the time information and the temperature difference value between the highest temperature information and the initial temperature information;
s210, presetting a temperature change rate threshold value, judging whether the temperature change rate is greater than the temperature change rate threshold value, and if so, proving that the heat dissipation capacity of the intelligent wearable equipment does not reach a preset standard;
and S212, generating correction information, and correcting the thickness of the target silica gel material in the intelligent wearable device through the correction information.
It should be noted that the invention further includes that if the reliability of the target silica gel material after the thickness correction does not reach the preset standard, the corrected contact area meets the heat dissipation requirement of the intelligent terminal, specifically, the invention further includes that
Performing bending reliability test on the target silica gel material after the thickness correction is finished, and acquiring the maximum bending frequency information of the target silica gel material after the thickness correction is finished; comparing and judging the maximum bending times information with the original reliability, and calculating the reliability deviation rate before and after thickness correction; judging whether the reliability deviation rate is within a preset range, and if so, taking the thickness information of the target silica gel material after the thickness correction as the preset thickness of the target silica gel material on the intelligent wearable device; if the target silica gel material is not in the position, the reliability of the target silica gel material after the thickness correction cannot meet the reliability standard, the target silica gel material is corrected again to generate the parcel area correction information, and the heat dissipation requirement of the intelligent terminal is met by correcting the contact area of the target silica gel material.
According to an embodiment of the present invention, the present invention further comprises: constructing an intelligent wearable product database,
constructing an intelligent wearable product database, wherein the intelligent wearable product database comprises historical inventory intelligent wearable product silica gel material heat dissipation data of various models and specifications;
carrying out similarity comparison in the intelligent wearable product database according to the obtained preset temperature change rate and product parameter data of the intelligent wearable product to be detected, and obtaining historical inventory intelligent wearable products, of which the similarity with the temperature change rate and the product parameter data of the intelligent wearable product to be detected in the intelligent wearable product database meets the preset value requirement;
taking the obtained thickness and wrapping area information of the silica gel material of the intelligent wearing product in the historical stock as the silica gel heat dissipation inspection standard of the intelligent wearing product to be detected;
if the silica gel material thickness and the parcel area information of the intelligent wearable product to be inspected do not satisfy the silica gel material thickness and the preset threshold range of the parcel area information of the intelligent wearable product in the historical inventory, the product to be inspected is defined as an unqualified product.
It should be noted that, in order to increase the way of obtaining the product inspection standard, an intelligent wearable product database is established, wherein the intelligent wearable product database comprises the heat dissipation data of the silica gel materials of historical inventory intelligent wearable products of various models and specifications, and the product heat dissipation data comprises the temperature change rate of the silica gel materials in the intelligent wearable products and product parameter data; and performing similarity comparison in a database according to the preset temperature change rate of the intelligent wearable product to be detected and the product parameter data, wherein the similarity comparison can be Euclidean distance or cosine comparison, and searching historical stock products meeting the preset value requirement in the product database.
Fig. 3 shows a block diagram of an automated detection system for smart wearable silicone materials in accordance with the present invention.
The second aspect of the present invention also provides an automatic detection system 3 for a silica material for intelligent wearing, which includes: the automatic detection method program for the intelligent wearable silica material comprises a memory 31 and a processor 32, wherein the memory comprises the automatic detection method program for the intelligent wearable silica material, and when the automatic detection method program for the intelligent wearable silica material is executed by the processor, the following steps are realized:
the method comprises the steps of obtaining hyperspectral image information of a target silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the target silica gel material;
constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics;
classifying the target silica gel material containing the defects according to the defect types, and judging whether the target silica gel material containing the defects can be subjected to secondary processing or not;
meanwhile, the thickness information of the target silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the target silica gel material is corrected according to the actual temperature change of the target silica gel material.
It should be noted that, the preprocessing is performed on the hyperspectral image information to obtain the spectral characteristics of the target silica gel material, and specifically the method comprises the following steps: acquiring hyperspectral image information of a target silica gel material, performing black-and-white correction processing on the hyperspectral image information to remove noise, acquiring an interested area in the hyperspectral image information through image segmentation, extracting spectral data of the interested area, processing the spectral data through a continuous projection method, eliminating collinearity among original data variables, and extracting an optimal response spectrum section; and extracting spectral characteristics according to the spectral data of the optimal response spectrum band.
It should be noted that, the constructing of the silica gel material defect detection model, and obtaining the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics specifically include:
establishing a neural network, establishing a silica gel material defect detection model, acquiring massive silica gel material spectral data containing internal defects and internal defects through big data, preprocessing and extracting spectral characteristics to generate a training set and a verification set; importing the training set into a silica gel material defect detection model for iterative training, and adjusting relevant parameters of the silica gel material defect detection model according to the iterative training; presetting an error threshold of a silica gel material defect detection model, and calculating the error of the silica gel material defect detection model after multiple iterative training; when the error is smaller than a preset error threshold value, performing a comparison test on the output result and the verification set, and when the deviation value is smaller than a preset deviation, obtaining a trained silica gel material defect detection model; and introducing the spectral characteristics of the target silica gel material into the trained silica gel material defect detection model, and analyzing the spectral characteristics through the silica gel material defect detection model to generate defect information of the silica gel sample.
It should be noted that, the classifying the target silica gel material containing the defect according to the defect type and determining whether the target silica gel material containing the defect can be subjected to the secondary processing specifically include:
classifying the target silica gel material containing the defects according to the internal defects and the surface defects to obtain defect characteristics, and matching the characteristic information with the target silica gel material containing the defects; acquiring position characteristics and size characteristics of the defects through the defect characteristics of a target silica gel material containing the surface defects, and evaluating the surface defects according to the position characteristics and the size characteristics according to a preset evaluation standard; judging whether the evaluation score of the surface defect is larger than a preset score threshold value or not, if so, judging the target silica gel material containing the surface defect as an unqualified product, and if not, performing secondary processing on the target silica gel material containing the surface defect; and judging the target silica gel material containing the internal defects as an unqualified product.
According to the embodiment of the invention, the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material, specifically:
acquiring the highest operating temperature of an intelligent terminal in the intelligent wearable device and the information of the wrapping area of a target silica gel material on the intelligent terminal;
obtaining the material heat conduction characteristic of a target silica gel material, and determining the thickness information of the silica gel material by combining the highest operation temperature and the wrapping area information with the material heat conduction characteristic;
acquiring initial temperature information of a silica gel material in the operation process of the intelligent wearable device, monitoring a target silica gel material in the operation process, and acquiring real-time temperature information of the silica gel material;
when the real-time temperature information reaches the highest temperature information, extracting time information used for temperature change, and calculating the temperature change rate of the target silica gel material according to the time information and the temperature difference value between the highest temperature information and the initial temperature information;
presetting a temperature change rate threshold, judging whether the temperature change rate is greater than the temperature change rate threshold, and if so, proving that the heat dissipation capacity of the intelligent wearable equipment does not reach a preset standard;
meanwhile, correction information is generated, and the thickness of the target silica gel material in the intelligent wearable device is corrected through the correction information.
It should be noted that the invention further includes that if the reliability of the target silica gel material after the thickness correction does not reach the preset standard, the corrected contact area meets the heat dissipation requirement of the intelligent terminal, specifically, the invention further includes that
Performing bending reliability test on the target silica gel material after the thickness correction is finished, and acquiring the maximum bending frequency information of the target silica gel material after the thickness correction is finished; comparing and judging the maximum bending times information with the original reliability, and calculating the reliability deviation rate before and after thickness correction; judging whether the reliability deviation rate is within a preset range, and if so, taking the thickness information of the target silica gel material after the thickness correction as the preset thickness of the target silica gel material on the intelligent wearable device; if the target silica gel material is not in the position, the reliability of the target silica gel material after the thickness correction cannot meet the reliability standard, the target silica gel material is corrected again to generate the parcel area correction information, and the heat dissipation requirement of the intelligent terminal is met by correcting the contact area of the target silica gel material.
According to an embodiment of the present invention, the present invention further comprises: constructing an intelligent wearable product database,
constructing an intelligent wearable product database, wherein the intelligent wearable product database comprises historical inventory intelligent wearable product silica gel material heat dissipation data of various models and specifications;
carrying out similarity comparison in the intelligent wearable product database according to the obtained preset temperature change rate and product parameter data of the intelligent wearable product to be detected, and obtaining historical inventory intelligent wearable products, of which the similarity with the temperature change rate and the product parameter data of the intelligent wearable product to be detected in the intelligent wearable product database meets the preset value requirement;
taking the obtained thickness and wrapping area information of the silica gel material of the intelligent wearing product in the historical stock as the silica gel heat dissipation inspection standard of the intelligent wearing product to be detected;
if the silica gel material thickness and the parcel area information of the intelligent wearable product to be inspected do not satisfy the silica gel material thickness and the preset threshold range of the parcel area information of the intelligent wearable product in the historical inventory, the product to be inspected is defined as an unqualified product.
It should be noted that, in order to increase the way of obtaining the product inspection standard, an intelligent wearable product database is established, wherein the intelligent wearable product database comprises the heat dissipation data of the silica gel materials of historical inventory intelligent wearable products of various models and specifications, and the product heat dissipation data comprises the temperature change rate of the silica gel materials in the intelligent wearable products and product parameter data; and performing similarity comparison in a database according to the preset temperature change rate of the intelligent wearable product to be detected and the product parameter data, wherein the similarity comparison can be Euclidean distance or cosine comparison, and searching historical stock products meeting the preset value requirement in the product database.
The third aspect of the present invention further provides a computer-readable storage medium, where the computer-readable storage medium includes a program of an automatic detection method for a smart wearable silica material, and when the program of the automatic detection method for a smart wearable silica material is executed by a processor, the steps of the method for automatically detecting a smart wearable silica material as described in any one of the above are implemented.
The invention discloses an automatic detection method, a system and a storage medium for intelligent wearable silica materials, wherein the method comprises the following steps: the method comprises the steps of obtaining hyperspectral image information of a silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the silica gel material; constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics; classifying the silica gel material containing the defects according to the defect types, and judging whether the silica gel material containing the defects can be subjected to secondary processing; meanwhile, the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material. The invention carries out defect detection and heat dissipation reliability detection through the silica gel material for intelligent wearing, thereby improving the detection efficiency and reducing the rejection rate of the silica gel material.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only a logical functional division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units; can be located in one place or distributed on a plurality of network units; some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, all the functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: a mobile storage device, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
Alternatively, the integrated unit of the present invention may be stored in a computer-readable storage medium if it is implemented in the form of a software functional module and sold or used as a separate product. Based on such understanding, the technical solutions of the embodiments of the present invention may be essentially implemented or a part contributing to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the methods described in the embodiments of the present invention. And the aforementioned storage medium includes: a removable storage device, a ROM, a RAM, a magnetic or optical disk, or various other media that can store program code.
The above description is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention, and all the changes or substitutions should be covered within the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the appended claims.
Claims (10)
1. An automatic detection method for intelligent wearable silica materials is characterized by comprising the following steps:
the method comprises the steps of obtaining hyperspectral image information of a target silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the target silica gel material;
constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics;
classifying the target silica gel material containing the defects according to the defect types, and judging whether the target silica gel material containing the defects can be subjected to secondary processing or not;
meanwhile, the thickness information of the target silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the target silica gel material is corrected according to the actual temperature change of the target silica gel material.
2. The automatic detection method for the silica gel material for intelligent wearing according to claim 1, wherein the hyperspectral image information is preprocessed to obtain the spectral characteristics of the target silica gel material, and specifically comprises:
acquiring hyperspectral image information of a target silica gel material, performing black-and-white correction processing on the hyperspectral image information to perform denoising, acquiring an interested area in the hyperspectral image information through image segmentation, extracting spectral data of the interested area,
processing the spectral data by a continuous projection method, eliminating the collinearity among original data variables, and extracting an optimal response spectral band;
and extracting spectral characteristics according to the spectral data of the optimal response spectrum band.
3. The automatic detection method for the intelligent wearable silica gel material according to claim 1, wherein the silica gel material defect detection model is constructed, and the surface defect and the internal defect of the target silica gel material are obtained through the silica gel material defect detection model according to the spectral characteristics, specifically:
establishing a neural network, establishing a silica gel material defect detection model, acquiring massive silica gel material spectral data containing internal defects and internal defects through big data, preprocessing and extracting spectral characteristics to generate a training set and a verification set;
importing the training set into a silica gel material defect detection model for iterative training, and adjusting relevant parameters of the silica gel material defect detection model according to the iterative training;
presetting an error threshold of a silica gel material defect detection model, and calculating the error of the silica gel material defect detection model after multiple iterative training;
when the error is smaller than a preset error threshold value, performing a comparison test on the output result and the verification set, and when the deviation value is smaller than a preset deviation, obtaining a trained silica gel material defect detection model;
and introducing the spectral characteristics of the target silica gel material into the trained silica gel material defect detection model, and analyzing the spectral characteristics through the silica gel material defect detection model to generate defect information of the silica gel sample.
4. The automatic detection method for the intelligent wearable silica gel material according to claim 1, wherein the target silica gel material with the defects is classified according to the defect type, and whether the target silica gel material with the defects can be subjected to secondary processing is judged, specifically:
classifying the target silica gel material containing the defects according to the internal defects and the surface defects to obtain defect characteristics, and matching the characteristic information with the target silica gel material containing the defects;
acquiring position characteristics and size characteristics of the defects through the defect characteristics of a target silica gel material containing the surface defects, and evaluating the surface defects according to the position characteristics and the size characteristics according to a preset evaluation standard;
judging whether the evaluation score of the surface defect is larger than a preset score threshold value or not, if so, judging the target silica gel material containing the surface defect as an unqualified product, and if not, performing secondary processing on the target silica gel material containing the surface defect;
and judging the target silica gel material containing the internal defects as an unqualified product.
5. The automatic detection method for the silica gel material for intelligent wearing according to claim 1, wherein the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearing device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material, specifically:
acquiring the highest operating temperature of an intelligent terminal in the intelligent wearable device and the information of the wrapping area of a target silica gel material on the intelligent terminal;
obtaining the material heat conduction characteristic of a target silica gel material, and determining the thickness information of the silica gel material by combining the highest operation temperature and the wrapping area information with the material heat conduction characteristic;
acquiring initial temperature information of a silica gel material in the operation process of the intelligent wearable device, monitoring a target silica gel material in the operation process, and acquiring real-time temperature information of the silica gel material;
when the real-time temperature information reaches the highest temperature information, extracting time information used for temperature change, and calculating the temperature change rate of the target silica gel material according to the time information and the temperature difference value between the highest temperature information and the initial temperature information;
presetting a temperature change rate threshold, judging whether the temperature change rate is greater than the temperature change rate threshold, and if so, proving that the heat dissipation capacity of the intelligent wearable equipment does not reach a preset standard;
meanwhile, correction information is generated, and the thickness of the target silica gel material in the intelligent wearable device is corrected through the correction information.
6. The method for automatically detecting the silica material for intelligent wearing according to claim 5, further comprising:
performing bending reliability test on the target silica gel material after the thickness correction is finished, and acquiring the maximum bending frequency information of the target silica gel material after the thickness correction is finished;
comparing and judging the maximum bending times information with the original reliability, and calculating the reliability deviation rate before and after thickness correction;
judging whether the reliability deviation rate is within a preset range, and if so, taking the thickness information of the target silica gel material after the thickness correction as the preset thickness of the target silica gel material on the intelligent wearable device;
if the target silica gel material is not in the position, the reliability of the target silica gel material after the thickness correction cannot meet the reliability standard, the target silica gel material is corrected again to generate the parcel area correction information, and the heat dissipation requirement of the intelligent terminal is met by correcting the contact area of the target silica gel material.
7. An automated detection system for smart wearable silicone materials, the system comprising: the automatic detection method program for the intelligent wearable silica material comprises the following steps when executed by the processor:
the method comprises the steps of obtaining hyperspectral image information of a target silica gel material, preprocessing the hyperspectral image information, and obtaining spectral characteristics of the target silica gel material;
constructing a silica gel material defect detection model, and acquiring the surface defects and the internal defects of the target silica gel material through the silica gel material defect detection model according to the spectral characteristics;
classifying the target silica gel material containing the defects according to the defect types, and judging whether the target silica gel material containing the defects can be subjected to secondary processing or not;
meanwhile, the thickness information of the target silica gel material is determined according to the heat dissipation requirement of the intelligent wearable device, and the thickness information of the target silica gel material is corrected according to the actual temperature change of the target silica gel material.
8. The automatic detection system for the intelligent wearable silica gel material according to claim 7, wherein the silica gel material defect detection model is constructed, and the surface defect and the internal defect of the target silica gel material are obtained through the silica gel material defect detection model according to the spectral characteristics, specifically:
establishing a neural network, establishing a silica gel material defect detection model, acquiring massive silica gel material spectral data containing internal defects and internal defects through big data, preprocessing and extracting spectral characteristics to generate a training set and a verification set;
importing the training set into a silica gel material defect detection model for iterative training, and adjusting relevant parameters of the silica gel material defect detection model according to the iterative training;
presetting an error threshold of a silica gel material defect detection model, and calculating the error of the silica gel material defect detection model after multiple iterative training;
when the error is smaller than a preset error threshold value, performing a comparison test on the output result and the verification set, and when the deviation value is smaller than a preset deviation, obtaining a trained silica gel material defect detection model;
and introducing the spectral characteristics of the target silica gel material into the trained silica gel material defect detection model, and analyzing the spectral characteristics through the silica gel material defect detection model to generate defect information of the silica gel sample.
9. The automatic detection system for the silica gel material for the intelligent wearing according to claim 7, wherein the thickness information of the silica gel material is determined according to the heat dissipation requirement of the intelligent wearing device, and the thickness information of the silica gel material is corrected according to the actual temperature change of the silica gel material, specifically:
acquiring the highest operating temperature of an intelligent terminal in the intelligent wearable device and the information of the wrapping area of a target silica gel material on the intelligent terminal;
obtaining the material heat conduction characteristic of a target silica gel material, and determining the thickness information of the silica gel material by combining the highest operation temperature and the wrapping area information with the material heat conduction characteristic;
acquiring initial temperature information of a silica gel material in the operation process of the intelligent wearable device, monitoring a target silica gel material in the operation process, and acquiring real-time temperature information of the silica gel material;
when the real-time temperature information reaches the highest temperature information, extracting time information used for temperature change, and calculating the temperature change rate of the target silica gel material according to the time information and the temperature difference value between the highest temperature information and the initial temperature information;
presetting a temperature change rate threshold, judging whether the temperature change rate is greater than the temperature change rate threshold, and if so, proving that the heat dissipation capacity of the intelligent wearable equipment does not reach a preset standard;
meanwhile, correction information is generated, and the thickness of the target silica gel material in the intelligent wearable device is corrected through the correction information.
10. A computer-readable storage medium characterized by: the computer readable storage medium includes a program for an automated detection method of smart wearable silicone material, which when executed by a processor, implements the steps of the method of any one of claims 1 to 6.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116030061A (en) * | 2023-03-29 | 2023-04-28 | 深圳市捷超行模具有限公司 | Silica gel molding effect detection method based on vision |
| WO2024187356A1 (en) * | 2023-03-14 | 2024-09-19 | 广州盛创文化发展有限公司 | Defect detection method and apparatus for silicone product, and terminal device and medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109001213A (en) * | 2018-07-19 | 2018-12-14 | 华南理工大学 | A kind of reel-to-reel ultrathin flexible IC exterior substrate detection method |
| CN210071685U (en) * | 2019-05-31 | 2020-02-14 | 王唐(苏州)机器人智能科技有限公司 | Rubber defect detecting machine |
| CN111507939A (en) * | 2020-03-12 | 2020-08-07 | 深圳大学 | Method and device for detecting external defect types of fruits and terminal |
| CN113834852A (en) * | 2021-11-25 | 2021-12-24 | 单县多米石墨烯科技有限公司 | Method and system for detecting heat dissipation performance of product with graphene coating |
-
2022
- 2022-03-16 CN CN202210257407.7A patent/CN114419034B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109001213A (en) * | 2018-07-19 | 2018-12-14 | 华南理工大学 | A kind of reel-to-reel ultrathin flexible IC exterior substrate detection method |
| CN210071685U (en) * | 2019-05-31 | 2020-02-14 | 王唐(苏州)机器人智能科技有限公司 | Rubber defect detecting machine |
| CN111507939A (en) * | 2020-03-12 | 2020-08-07 | 深圳大学 | Method and device for detecting external defect types of fruits and terminal |
| CN113834852A (en) * | 2021-11-25 | 2021-12-24 | 单县多米石墨烯科技有限公司 | Method and system for detecting heat dissipation performance of product with graphene coating |
Non-Patent Citations (2)
| Title |
|---|
| 贾敏: "高光谱成像系统的设计与实验研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》 * |
| 贾敏: "高光谱成像系统的设计与实验研究", 《中国优秀硕士学位论文全文数据库（电子期刊）》, no. 08, 15 August 2018 (2018-08-15), pages 138 - 578 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024187356A1 (en) * | 2023-03-14 | 2024-09-19 | 广州盛创文化发展有限公司 | Defect detection method and apparatus for silicone product, and terminal device and medium |
| CN116030061A (en) * | 2023-03-29 | 2023-04-28 | 深圳市捷超行模具有限公司 | Silica gel molding effect detection method based on vision |
| CN116030061B (en) * | 2023-03-29 | 2023-08-22 | 深圳市捷超行模具有限公司 | Silica gel molding effect detection method based on vision |
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Inventor after: Huang Xin Inventor after: Chen Jianping Inventor after: Zhang Yuhui Inventor after: Cheng Li Inventor before: Huang Xin Inventor before: Zhan Jianping Inventor before: Zhang Yuhui Inventor before: Cheng Li |