CN115575322A - Outgoing quality inspection method and system for direct-insertion LED lamp beads - Google Patents
Outgoing quality inspection method and system for direct-insertion LED lamp beads Download PDFInfo
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Abstract
The invention discloses a delivery quality inspection method and a delivery quality inspection system for a direct-insert LED lamp bead, wherein a first detection side weight distribution list is obtained according to first historical detection statistical information and second historical detection statistical information; obtaining a first side weight compensation parameter set according to the quality inspection product influence information, and correcting a first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list; data collection is carried out on the basis of the image collection device and the temperature collection device through a second detection side weight distribution list, delivery sampling test of the direct-insert LED lamp beads is carried out on the basis of the first image collection and the first temperature collection set, and a first sampling test result is obtained; and performing factory pre-warning of the direct-insert LED lamp beads according to the first sampling inspection result. The technical problem that in the prior art, in the process of delivery quality inspection of the direct-insert LED lamp beads, delivery quality inspection cannot be accurately performed according to the production characteristics of the direct-insert LED lamp beads is solved.
Description
Technical Field
The invention relates to the field of intelligent detection of LED lamp beads, in particular to a factory quality inspection method and system of a direct-insert LED lamp bead.
Background
The direct-insert LED is a plug-in light-emitting diode, and is an energy-saving and environment-friendly lighting semiconductor device for converting electric energy into visible light. In the production and manufacturing process of the in-line LED lamp beads, the produced LED lamp beads need to be subjected to delivery quality inspection so as to ensure the quality of the supply of the LED lamp beads.
However, in the process of implementing the technical scheme of the invention in the application, the technology at least has the following technical problems:
in the prior art, in the process of delivery quality inspection of the direct-insert LED lamp beads, the technical problem that delivery quality inspection cannot be accurately performed according to the production characteristics of the direct-insert LED lamp beads exists.
Disclosure of Invention
The application solves the technical problem that in the process of outgoing quality inspection of the direct-insert LED lamp beads in the prior art, accurate outgoing quality inspection cannot be carried out according to the production characteristics of the direct-insert LED lamp beads, the production characteristics of the direct-insert LED lamp beads are combined, intelligent targeted outgoing spot inspection is carried out, quality inspection workload is saved, and the technical effect of quality inspection accuracy is improved.
In view of the above problems, the present application provides a factory quality inspection method and system for a direct-insert LED lamp bead.
In a first aspect, the application provides a factory quality inspection method for a direct-insert LED lamp bead, the method is applied to an intelligent factory quality inspection system, the intelligent factory quality inspection system is in communication connection with an image acquisition device and a temperature acquisition device, and the method comprises the following steps: obtaining first historical detection statistical information, wherein the first historical detection statistical information is historical production statistical information of a first company; obtaining second historical detection statistical information, wherein the second historical detection statistical information is historical production statistical information of a second company, and obtaining a first detection side weight distribution list according to the first historical detection statistical information and the second historical detection statistical information; obtaining a first side weight compensation parameter set according to quality inspection product influence information, and correcting the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list; acquiring data based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set; performing factory selective inspection test on the direct-insertion LED lamp beads based on the first image set and the first temperature acquisition set to obtain a first selective inspection result; and performing factory pre-warning of the direct-insert LED lamp beads according to the first sampling inspection result.
On the other hand, this application still provides a system of leaving factory quality inspection of cut straightly LED lamp pearl, the system includes: a first obtaining unit, configured to obtain first historical detection statistical information, where the first historical detection statistical information is historical production statistical information of a first company; a second obtaining unit, configured to obtain second historical detection statistical information, where the second historical detection statistical information is historical production statistical information of a second company, and obtain a first detection side weight distribution list according to the first historical detection statistical information and the second historical detection statistical information; a third obtaining unit, configured to obtain a first side weight compensation parameter set according to quality inspection product influence information, and correct the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list; a fourth obtaining unit, configured to perform data acquisition based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list, to obtain a first image set and a first temperature acquisition set; a fifth obtaining unit, configured to perform factory selective inspection testing on the in-line LED lamp beads based on the first image set and the first temperature acquisition set, and obtain a first selective inspection result; and the first early warning unit is used for carrying out factory early warning on the direct-insert LED lamp beads according to the first sampling inspection result.
In a third aspect, the present invention provides an electronic device, comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the method according to any one of the first aspect when executing the program.
In a fourth aspect, the present application provides a computer program product comprising a computer program and/or instructions which, when executed by a processor, performs the steps of the method of any one of the first aspect.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
obtaining first historical detection statistical information, wherein the first historical detection statistical information is historical production statistical information of a first company; obtaining second historical detection statistical information, wherein the second historical detection statistical information is historical production statistical information of a second company, and detecting side weight distribution of the problem of the company and the common problem of other companies is carried out according to the first historical detection statistical information and the second historical detection statistical information to obtain a first detecting side weight distribution list; obtaining a first side weight compensation parameter set according to quality inspection product influence information, and correcting the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list; acquiring data based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set; performing factory selective inspection test on the direct-insertion LED lamp beads based on the first image set and the first temperature acquisition set to obtain a first selective inspection result; and performing factory pre-warning of the direct-insert LED lamp beads according to the first sampling result, so that the production characteristics of the direct-insert LED lamp beads are combined, intelligent targeted factory sampling is performed, the quality inspection workload is saved, and the technical effect of quality inspection accuracy is improved.
The foregoing description is only an overview of the technical solutions of the present application, and the present application can be implemented according to the content of the description in order to make the technical means of the present application more clearly understood, and the following detailed description of the present application is given in order to make the above and other objects, features, and advantages of the present application more clearly understandable.
Drawings
Fig. 1 is a schematic flow chart of a delivery quality inspection method for a direct-insert LED lamp bead according to the present application;
fig. 2 is a schematic flow chart illustrating a first sampling inspection parameter determination process in the delivery quality inspection method for a direct-insert LED lamp bead according to the present application;
fig. 3 is a schematic flow chart of image matching identification in the delivery quality inspection method for the in-line LED lamp bead according to the present application;
fig. 4 is a schematic flow chart of a construction of an abnormal feature identification list of the delivery quality inspection method for a direct-insert LED lamp bead according to the present application;
fig. 5 is a schematic structural diagram of a factory quality inspection system of a direct-insert LED lamp bead according to the present application;
fig. 6 is a schematic structural diagram of an electronic device according to the present application.
Description of reference numerals: the system comprises a first obtaining unit 11, a second obtaining unit 12, a third obtaining unit 13, a fourth obtaining unit 14, a fifth obtaining unit 15, a first early warning unit 16, an electronic device 50, a processor 51, a memory 52, an input device 53 and an output device 54.
Detailed Description
The application solves the technical problem that in the prior art, in the process of delivery quality inspection of the direct-insert LED lamp bead, accurate delivery quality inspection cannot be carried out according to the production characteristics of the direct-insert LED lamp bead, the production characteristics of the direct-insert LED lamp bead are combined, intelligent targeted delivery spot inspection is carried out, quality inspection workload is saved, and the technical effect of quality inspection accuracy is improved. Embodiments of the present application are described below with reference to the accompanying drawings. As can be appreciated by those skilled in the art, with the development of technology and the emergence of new scenarios, the technical solutions provided in the present application are also applicable to similar technical problems.
The terms "first," "second," and the like in the description and claims of the present application and in the foregoing drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the terms so used are interchangeable under appropriate circumstances and are merely descriptive of the various embodiments of the application and how objects of the same nature can be distinguished. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of elements is not necessarily limited to those elements, but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Summary of the application
In the production and manufacturing process of the LED lamp beads, the product is required to be subjected to factory detection in order to ensure the product quality. However, in the prior art, in the process of factory quality inspection of the in-line LED lamp bead, there is a technical problem that the factory quality inspection cannot be accurately performed according to the production characteristics of the in-line LED lamp bead.
In view of the above technical problems, the technical solution provided by the present application has the following general idea:
the application provides a delivery quality inspection method of a direct-insert LED lamp bead, which is applied to an intelligent delivery quality inspection system, wherein the intelligent delivery quality inspection system is in communication connection with an image acquisition device and a temperature acquisition device, and the method comprises the following steps: obtaining first historical detection statistical information, wherein the first historical detection statistical information is historical production statistical information of a first company; obtaining second historical detection statistical information, wherein the second historical detection statistical information is historical production statistical information of a second company, and obtaining a first detection side weight distribution list according to the first historical detection statistical information and the second historical detection statistical information; obtaining a first side weight compensation parameter set according to quality inspection product influence information, and correcting the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list; acquiring data based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set; performing factory selective inspection test on the direct-insertion LED lamp beads based on the first image set and the first temperature acquisition set to obtain a first selective inspection result; and performing factory pre-warning of the direct-insert LED lamp beads according to the first sampling inspection result.
Having thus described the general principles of the present application, various non-limiting embodiments thereof will now be described in detail with reference to the accompanying drawings.
Example one
As shown in fig. 1, the application provides a factory quality inspection method for a direct-insert LED lamp bead, the method is applied to an intelligent factory quality inspection system, the intelligent factory quality inspection system is in communication connection with an image acquisition device and a temperature acquisition device, and the method includes:
step S100: obtaining first historical detection statistical information, wherein the first historical detection statistical information is historical production statistical information of a first company;
step S200: obtaining second historical detection statistical information, wherein the second historical detection statistical information is historical production statistical information of a second company, and obtaining a first detection weight distribution list according to the first historical detection statistical information and the second historical detection statistical information;
specifically, the intelligent factory quality inspection system is a system for performing sampling detection with emphasis in combination with the production detection characteristics of a product, and the image acquisition device is an electronic device capable of performing high-definition image sampling on the product, and is generally a CCD camera. The temperature acquisition device is equipment for carrying out direct insertion LED lamp pearl temperature acquisition monitoring, embeds temperature sensor, can carry out real-time temperature acquisition. The intelligent factory quality inspection system is in communication connection with the image acquisition device and the temperature acquisition device, and can perform mutual information interaction. The first company is a production company of the current in-line LED lamp beads, namely a target company for intelligent delivery detection, and the second company is another company except the first company. Under the permission of a client collaborating with a first company, combining the historical detection record of the first company to obtain the first historical detection statistical information, wherein the first historical detection statistical information comprises problem points, problem quantity, problem proportion and quality problems fed back by the collaborating company after the first company carries out historical factory random inspection on products. The first historical inspection statistical information reflects an emphasis characteristic of a production inspection of the first company. And the second historical detection statistical information is collected quality detection result statistical information of the LED lamp beads produced by the company which produces the in-line LED lamp beads except the first company under the premise that other companies permit publishing. And the second historical detection statistical information reflects the generic quality characteristic of the production detection of the in-line LED lamp beads. And performing the distribution of the emphasis points of the detection items of the quality inspection samples based on the first historical detection statistical information and the second historical detection statistical information. For example, the detection feature of the non-emphasis list has a first sampling quantity for detection, the detection item of the emphasis list is determined according to the processing sampling proportion and the emphasis proportion according to different emphasis proportions, and the factory spot check is more targeted to the production detection of the LED lamp beads through the determination of the first detection emphasis distribution list, so that the detection result is more accurate.
Step S300: obtaining a first side weight compensation parameter set according to quality inspection product influence information, and correcting the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list;
specifically, the quality inspection product information is information of a characteristic quantity distribution of quality problems of production quality inspection in the process of producing and manufacturing the in-line LED lamp bead by the first company. And obtaining the influence information of the quality inspection product by calling the production record of the first company on the production of the in-line LED lamp beads. And obtaining the first side weight compensation parameter set according to the occurrence frequency of each quality characteristic of the quality inspection product influence information and the influence coefficient of the quality characteristic on the product, correcting the first detection side weight distribution list based on the first side weight compensation parameter set, and obtaining the second detection side weight distribution list according to the correction result. Through the acquisition of the first side weight compensation parameter set, the quality of the in-line LED lamp beads produced by the first company is further deeply analyzed, so that the obtained second detection side weight distribution list is more attached to products, less detection resources can be consumed, and the technical effect of more comprehensive and accurate detection on outgoing products is achieved.
Step S400: acquiring data based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set;
step S500: performing factory selective inspection test on the direct-insertion LED lamp beads based on the first image set and the first temperature acquisition set to obtain a first selective inspection result;
specifically, the image acquisition device and the temperature acquisition device are controlled to sample and acquire factory products through the intelligent factory quality inspection system according to the second detection weight distribution list. And acquiring the first image set and the first temperature set according to corresponding image acquisition and temperature acquisition of the sampled samples.
Further, in the process of sampling samples based on the second detection-side weight distribution list, after the analysis of the factory quantity and factory batch of the current factory product is required, the quantity of samples is determined. When the ratio of the factory batch in the factory samples is higher than the ratio of the factory quantity, determining the factory sample quantity according to the sampling inspection standard of the factory batch; and when the ratio of the factory quantity in the factory samples is higher than that of the factory batches, determining the sample quantity of the factory products according to the sampling inspection standard of the factory quantity. After the sampling quantity of basic samples is determined according to the quantity of delivered products, the samples are detected and expanded on the basis of the second detection side weight distribution list, detection items are distributed according to expansion results, corresponding quality detection data acquisition is carried out through the image acquisition device and the temperature acquisition device on the basis of item distribution results, a first image set and a first temperature acquisition set are obtained, corresponding item image characteristic analysis is carried out according to the first image set, and the quality sampling inspection result of the images is obtained; and carrying out temperature analysis on the first temperature set to obtain a temperature test result. And butterfly the first sampling inspection result according to the test result.
Step S600: and performing factory pre-warning of the direct-insert LED lamp beads according to the first sampling inspection result.
Specifically, according to the quality spot-check condition in the first spot-check result, factory early warning is carried out on factory products. For example, when there is no sample with abnormal quality in the factory sample inspection sample, but a certain characteristic parameter of a plurality of sample samples is close to an abnormal value, an early warning of re-sampling detection of the corresponding characteristic is required at this time. When a sample with abnormal quality exists in an ex-factory sampling sample, batch positioning of the product is needed, early warning of comprehensive detection of the quality of the product in the same batch is carried out, and through detection and early warning of the sample, production characteristics of the LED lamp beads in direct insertion are combined, and intelligent targeted ex-factory sampling is carried out, so that quality inspection workload is saved, and the technical effects of improving quality inspection accuracy are achieved
Further, as shown in fig. 2, step S400 of the present application further includes:
step S410: obtaining first quantity information of outgoing straight LED lamp beads;
step S420: obtaining first production batch information through production parameters of the LED lamp beads which are directly inserted;
step S430: determining a first sample sampling inspection constraint parameter according to the first quantity information and the first production batch information;
step S440: obtaining a first sampling inspection parameter based on the first sample sampling inspection constraint parameter and the second detection side weight distribution list;
step S450: obtaining the first set of images and a first set of temperature acquisitions based on the first spot check parameter.
Specifically, the first quantity information is quantity information of products which are currently shipped from a factory of a first company, and when the first company leaves the factory of the products, the product information which is shipped from the factory is obtained through the intelligent shipped quality inspection system, and the product information includes product numbers, product batches and total product quantity information. And obtaining the first quantity information and the first production batch information through the intelligent factory quality inspection system. Obtaining the sampling quantity information of the samples according to the first quantity information according to the quantity sampling constraint rule of the samples; and obtaining the batch sample sampling quantity distribution information according to the batch sample sampling standard of the same batch of samples in the production batch. And determining the first sample sampling inspection constraint parameter according to the sampling inspection quantity information and the batch sample sampling inspection quantity distribution information, and redistributing and distributing the actual sample sampling inspection parameters based on the first sample sampling inspection constraint parameter and the second detection side redistribution list to obtain the first sampling inspection parameter. And after a sample is selected through the first sampling inspection parameter, corresponding data acquisition is carried out through the image acquisition device and the temperature acquisition device, and the first image set and the first temperature acquisition set are obtained.
Further, as shown in fig. 3, step S500 of the present application further includes:
step S510: obtaining a first sampling product according to the first sampling parameter;
step S520: acquiring a multi-angle image of the first sampling product through the image acquisition device to obtain a first image set;
step S530: constructing an abnormal feature identification list of the direct-insert LED lamp beads;
step S540: performing feature matching identification on the first image set based on the abnormal feature identification list to obtain a first matching identification result;
step S550: and obtaining the first sampling inspection result through the first matching identification result.
Specifically, sampling of products is carried out according to the first sampling parameters, and a first sampling product is obtained and has a plurality of items to be tested. The anomalous feature identification list includes a plurality of features including, for example: bubble features, smudge features, appearance imperfections features, burr features, pin length imperfections features, and the like. And constructing the abnormal feature identification list according to the features. The image acquisition device is used for acquiring images of the first sampling product, the image acquisition is multi-angle image acquisition, the images acquired by the multi-angle image acquisition have position identification features which are related to each other in position, the abnormal feature matching of the first image set is performed through the abnormal feature identification list, and the sampling inspection test of the first sampling product is completed according to the matching degree information and the matched abnormal feature information. And after the feature matching of the corresponding distribution features is carried out on all sampled products of the factory products, the first sampling inspection result is obtained according to the matching result. Through the multi-angle image acquisition of image acquisition device, carry out feature recognition to the image acquisition result, and then make more intelligent, the automation to the selective examination of product, reach intelligence and carry out the selective examination of product, and then realize the technical effect who improves the accuracy and the efficiency of the detection that dispatches from the factory of product.
Further, as shown in fig. 4, the step 530 of the present application further includes:
step S531: acquiring bubble pictures of the direct-insert LED lamp beads through big data to obtain a first acquisition result;
step S532: carrying out artificial bubble grade identification on the first acquisition result to obtain a first identification result;
step S533: acquiring a sealing glue picture of the direct-insert LED lamp bead through big data to obtain a second acquisition result;
step S534: carrying out manual sealing grade identification on the second acquisition result to obtain a second identification result;
step S535: and constructing the abnormal feature recognition list through the first identification result and the second identification result.
Specifically, in the process of constructing the abnormal feature identification list, the abnormal features need to be collected and identified as comprehensively as possible to ensure the accuracy, reliability and comprehensiveness of the abnormal feature identification list, and the data collection is performed through circulating data to obtain the bubble picture set and the sealing glue picture set of the in-line LED lamp bead. And carrying out grade identification on the acquired image by taking intelligent equipment as assistance and taking artificial identification as a leading part, and obtaining a first identification result and a second identification result according to the identification result, wherein the first identification result is a result of carrying out the grade identification of bubbles of the first acquisition result, and the second identification result is a result of carrying out the grade identification of sealing glue of the second acquisition result. Through the identification image characteristics of a plurality of characteristics, a plurality of characteristics are identified in a grading way, so that the characteristics in the image identification process are richer, the characteristic identification matching of the image is more accurate, and the technical effect of improving the quality inspection accuracy of factory quality inspection is achieved.
Further, the performing feature matching and recognition on the first image set based on the abnormal feature recognition list further includes:
step S541: performing feature matching identification on the first image set based on the abnormal feature identification list to obtain a first matching degree list of first matching features;
step S542: sequentially sorting the first matching degree list to obtain a first sorting result;
step S543: performing correlation analysis of adjacent matching degrees on the first matching degree in the first sequencing result to obtain a first correlation analysis result;
step S544: and performing matching degree distribution of the first sequencing result based on the first correlation analysis result, and obtaining a first matching identification result according to the matching degree distribution result.
Specifically, in the process of matching image features through the abnormal feature identification list, feature matching is performed through the intelligent factory quality inspection system based on the abnormal feature identification list according to detection features selected by a preset sample. For example, when feature matching is performed, matching the first bubble feature, the sample determines that a bubble quality inspection anomaly feature is present. And identifying a plurality of bubbles in the list according to the abnormal features, comparing the bubbles of the sample according to types and sizes, and sequencing the features of the existing bubbles according to the similarity of the bubbles of the sample and the matching degree to obtain the first sequencing result.
Further, each matching bubble feature in the first sequencing result is further analyzed. Firstly, extracting the highest matching degree in the first sequencing result and the feature and the bubble grade corresponding to the feature of the adjacent matching degree (second matching degree), and analyzing the association degree of the first matching degree and the second matching degree, wherein the analysis of the association degree is to judge whether the difference value of the matching degrees between the first matching degree and the second matching degree meets an expected threshold value, when the difference value of the first matching degree and the second matching degree does not meet the expected threshold value, the matching of the second matching degree is not in an expected analysis range, and at the moment, the calculation of the first matching identification result is only carried out according to the first matching degree and the grade feature of the bubble corresponding to the first matching degree; when the difference between the first matching degree and the second matching degree meets an expected threshold, the first matching degree and the second matching degree are indicated to need to be further analyzed simultaneously. And then, obtaining a third matching degree according to the first sequencing result, judging whether the matching result of the second matching degree and the third matching degree meets an expected threshold value, if not, distributing the matching degrees according to the first matching degree, the second matching degree and the characteristic grades corresponding to the first matching degree and the second matching degree, and obtaining the first matching identification result based on the distribution result. By carrying out detailed analysis on the association degree of the matching degree, the matching analysis result of the features is more accurate, and a basis is provided for obtaining more accurate factory quality inspection results and tamping, so that data support is provided.
Further, step S544 of the present application further includes:
step S5441: obtaining a first matching degree association preset threshold;
step S5442: judging whether the first matching degree and the second matching degree meet a first matching degree association preset threshold value or not, wherein the second matching degree is the adjacent matching degree of the first matching degree in the first sequencing result;
step S5443: and when the first matching degree and the second matching degree meet a preset threshold value associated with the first matching degree, obtaining the first matching identification result based on the first matching degree, the second matching degree and corresponding matching features.
Specifically, the first matching degree association preset threshold is a condition for determining the association degree between matching degrees, in the first ordering result, the matching degrees are ordered from high to low, and are sequentially a first matching degree, a second matching degree, and a third matching degree …, firstly, the association determination of the first matching degree and the second matching degree is performed according to the first matching degree association preset threshold, when the association of the first matching degree and the second matching degree does not satisfy the first matching degree association preset threshold, the determination is not performed continuously, and the determination of the first matching identification result is performed only according to the feature level and the matching degree corresponding to the first matching degree. When the association between the first matching degree and the second matching degree meets the preset threshold of the association between the first matching degree and the second matching degree, the association between the second matching degree and the third matching degree needs to be determined continuously, and so on, until the matching degree does not meet the preset threshold of the association between the first matching degree or meets the expected number. And obtaining the first matching identification result according to the matching degree meeting the first matching degree correlation preset threshold value and the matched corresponding characteristic grade. By carrying out detailed analysis on the matching degree, the obtained final matching identification result is more intelligent and accurate, and the technical effect of improving the accuracy of the early warning of factory quality inspection is achieved.
Further, step S400 of the present application further includes:
step S460: obtaining a first temperature control parameter;
step S470: sampling direct-insert LED lamp bead test is carried out through the first temperature control parameter, data acquisition is carried out through the temperature acquisition device, and a first temperature acquisition set is obtained.
Specifically, the first temperature control parameter is a temperature control parameter for performing a reliability test, and includes a parameter of a specific environmental temperature, a power-on time control parameter, a power-on power control parameter, a position parameter of a sampling point, and the like, the test environment is set by the first temperature control parameter, after the setting is completed, data acquisition is performed by the temperature acquisition devices distributed with the test positions, and the first temperature acquisition set is obtained according to an acquisition result. And carrying out factory-leaving temperature spot check early warning of the direct-insertion LED lamp beads based on the first temperature collection set.
In summary, the outgoing quality inspection method and system for the in-line LED lamp bead provided by the present application have the following technical effects:
1. obtaining first historical detection statistical information, wherein the first historical detection statistical information is historical production statistical information of a first company; obtaining second historical detection statistical information, wherein the second historical detection statistical information is historical production statistical information of a second company, and detecting side weight distribution of the problem of the company and the common problem of other companies is carried out according to the first historical detection statistical information and the second historical detection statistical information to obtain a first detecting side weight distribution list; obtaining a first side weight compensation parameter set according to quality inspection product influence information, and correcting the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list; acquiring data based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set; performing factory selective inspection test on the direct-insertion LED lamp beads based on the first image set and the first temperature acquisition set to obtain a first selective inspection result; and performing factory early warning on the direct-insert LED lamp beads according to the first random inspection result, achieving the technical effects of combining the production characteristics of the direct-insert LED lamp beads, performing intelligent targeted factory random inspection, saving quality inspection workload and improving quality inspection accuracy.
2. Due to the fact that the multi-angle image acquisition is achieved through the image acquisition device, the image acquisition result is subjected to characteristic recognition, the product sampling inspection is more intelligent and automatic, the product sampling inspection is intelligently achieved, and the technical effects of improving the accuracy and the efficiency of factory inspection of the product are achieved.
3. Because the mode of carrying out hierarchical identification on the multiple features by constructing the image recognition features of the multiple features is adopted, the features in the image recognition process are richer, the image feature recognition can be more accurately matched, and the technical effect of improving the quality inspection accuracy of factory quality inspection is realized.
4. Due to the adoption of the mode of carrying out detailed analysis on the relevance of the matching degree, the matching analysis result of the characteristics is more accurate, and a basis is provided for obtaining more accurate factory quality inspection results and tamping data.
5. Due to the adoption of the mode of carrying out detailed analysis on the matching degree, the obtained final matching identification result is more intelligent and accurate, and the technical effect of improving the accuracy of the early warning of the factory quality inspection is further realized.
Example two
Based on the same inventive concept as the outgoing quality inspection method of the direct-insert LED lamp bead in the foregoing embodiment, the present invention further provides an outgoing quality inspection system of the direct-insert LED lamp bead, as shown in fig. 5, the system includes:
a first obtaining unit 11, where the first obtaining unit 11 is configured to obtain first historical detection statistical information, where the first historical detection statistical information is historical production statistical information of a first company;
a second obtaining unit 12, where the second obtaining unit 12 is configured to obtain second historical detection statistical information, where the second historical detection statistical information is historical production statistical information of a second company, and obtain a first detection-side weight distribution list according to the first historical detection statistical information and the second historical detection statistical information;
a third obtaining unit 13, where the third obtaining unit 13 is configured to obtain a first side weight compensation parameter set according to quality inspection product influence information, and correct the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list;
a fourth obtaining unit 14, where the fourth obtaining unit 14 is configured to perform data acquisition based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set;
a fifth obtaining unit 15, where the fifth obtaining unit 15 is configured to perform a factory sampling test on the direct-insert LED lamp bead based on the first image set and the first temperature acquisition set, and obtain a first sampling test result;
and the first early warning unit 16 is used for performing factory early warning on the direct-insert LED lamp beads according to the first sampling inspection result.
Further, the system further comprises:
a sixth obtaining unit, configured to obtain first quantity information of outgoing straight-inserted LED lamp beads;
a seventh obtaining unit, configured to obtain first production batch information according to production parameters of the in-line LED lamp beads;
a first determining unit, configured to determine a first sample spot check constraint parameter according to the first quantity information and the first production batch information;
an eighth obtaining unit, configured to obtain a first sampling parameter based on the first sample sampling constraint parameter and the second detection side redistribution list;
a ninth obtaining unit for obtaining the first set of images and a first set of temperature acquisitions based on the first spot check parameter.
Further, the system further comprises:
a tenth obtaining unit, configured to obtain a first sampled product according to the first sampling parameter;
an eleventh obtaining unit, configured to perform multi-angle image acquisition on the first sampled product through the image acquisition device, to obtain the first image set;
the first construction unit is used for constructing an abnormal feature identification list of the direct-insert LED lamp beads;
a twelfth obtaining unit, configured to perform feature matching recognition on the first image set based on the abnormal feature recognition list, so as to obtain a first matching recognition result;
a thirteenth obtaining unit, configured to obtain the first sampling result through the first matching identification result.
Further, the system further comprises:
the fourteenth obtaining unit is used for acquiring a bubble picture of the direct-insert LED lamp bead through big data to obtain a first acquisition result;
a fifteenth obtaining unit, configured to perform artificial bubble level identification on the first acquisition result to obtain a first identification result;
the sixteenth obtaining unit is used for acquiring a sealing glue picture of the direct-insert LED lamp bead through big data to obtain a second acquisition result;
a seventeenth obtaining unit, configured to perform manual sealant grade identification on the second acquisition result to obtain a second identification result;
a second constructing unit, configured to construct the abnormal feature recognition list by using the first identification result and the second identification result.
Further, the system further comprises:
an eighteenth obtaining unit, configured to perform feature matching recognition on the first image set based on the abnormal feature recognition list, and obtain a first matching degree list of first matching features;
a nineteenth obtaining unit, configured to perform sequential ordering on the first matching degree list, and obtain a first ordering result;
a twentieth obtaining unit, configured to perform correlation analysis of neighboring matching degrees on the first matching degree in the first ranking result to obtain a first correlation analysis result;
a twenty-first obtaining unit, configured to perform matching degree distribution of the first ranking result based on the first correlation analysis result, and obtain a first matching identification result according to a matching degree distribution result.
Further, the system further comprises:
a twenty-second obtaining unit, configured to obtain a first matching degree associated preset threshold;
a first judging unit, configured to judge whether the first matching degree and a second matching degree satisfy a preset threshold associated with the first matching degree, where the second matching degree is an adjacent matching degree to the first matching degree in the first sorting result;
a twenty-third obtaining unit, configured to, when the first matching degree and the second matching degree satisfy the first matching degree associated preset threshold, obtain the first matching identification result based on the first matching degree, the second matching degree, and corresponding matching features.
Further, the system further comprises:
a twenty-fourth obtaining unit for obtaining a first temperature control parameter;
and the twenty-fifth obtaining unit is used for sampling and directly inserting the LED lamp bead test through the first temperature control parameter, and acquiring data through the temperature acquisition device to obtain the first temperature acquisition set.
Various changes and specific examples of the factory quality inspection method for the in-line LED lamp bead in the first embodiment of fig. 1 are also applicable to the factory quality inspection system for the in-line LED lamp bead in this embodiment, and through the foregoing detailed description of the factory quality inspection method for the in-line LED lamp bead, a person skilled in the art can clearly know the implementation method of the factory quality inspection system for the in-line LED lamp bead in this embodiment, so for the brevity of the description, detailed description is not given here.
Exemplary electronic device
The electronic device of the present application is described below with reference to fig. 6.
Fig. 6 illustrates a schematic structural diagram of an electronic device according to the present application.
Based on the inventive concept of the delivery quality inspection method for the in-line LED lamp bead in the foregoing embodiment, the invention further provides an electronic device, and the electronic device according to the application is described below with reference to fig. 6. The electronic device may be a removable device itself or a stand-alone device independent thereof, on which a computer program is stored which, when being executed by a processor, carries out the steps of any of the methods as described hereinbefore.
As shown in fig. 6, the electronic device 50 includes one or more processors 51 and a memory 52.
The processor 51 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the electronic device 50 to perform desired functions.
The memory 52 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium and executed by the processor 51 to implement the methods of the various embodiments of the application described above and/or other desired functions.
In one example, the electronic device 50 may further include: an input device 53 and an output device 54, which are interconnected by a bus system and/or other form of connection mechanism (not shown).
The delivery quality inspection method of the direct-insertion LED lamp bead provided by the embodiment of the invention is applied to an intelligent delivery quality inspection system, wherein the intelligent delivery quality inspection system is in communication connection with an image acquisition device and a temperature acquisition device, and the method comprises the following steps: obtaining first historical detection statistical information, wherein the first historical detection statistical information is historical production statistical information of a first company; obtaining second historical detection statistical information, wherein the second historical detection statistical information is historical production statistical information of a second company, and obtaining a first detection side weight distribution list according to the first historical detection statistical information and the second historical detection statistical information; obtaining a first side weight compensation parameter set according to quality inspection product influence information, and correcting the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list; acquiring data based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set; performing factory selective inspection test on the direct-insertion LED lamp beads based on the first image set and the first temperature acquisition set to obtain a first selective inspection result; and performing factory pre-warning of the direct-insert LED lamp beads according to the first sampling inspection result. The technical problem that in the prior art, in the process of outgoing quality inspection of the direct-insert LED lamp beads, accurate outgoing quality inspection cannot be carried out according to the production characteristics of the direct-insert LED lamp beads is solved, the production characteristics of the direct-insert LED lamp beads are combined, intelligent targeted outgoing spot inspection is carried out, quality inspection workload is saved, and the quality inspection accuracy is improved.
Through the above description of the embodiments, those skilled in the art will clearly understand that the present application can be implemented by software plus necessary general-purpose hardware, and certainly can also be implemented by special-purpose hardware including special-purpose integrated circuits, special-purpose CPUs, special-purpose memories, special-purpose components and the like. Generally, functions performed by computer programs can be easily implemented by corresponding hardware, and specific hardware structures for implementing the same functions may be various, such as analog circuits, digital circuits, or dedicated circuits. However, for the present application, the implementation of a software program is more preferable. Based on such understanding, the technical solutions of the present application may be substantially embodied in the form of a software product, which is stored in a readable storage medium, such as a floppy disk, a usb disk, a removable hard disk, a ROM, a RAM, a magnetic disk, or an optical disk of a computer, and includes several instructions for causing a computer device to execute the method according to the embodiments of the present application.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product.
The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the present application are generated, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted from a computer-readable storage medium to another computer-readable storage medium, which may be magnetic (e.g., floppy disks, hard disks, tapes), optical (e.g., DVDs), or semiconductor (e.g., solid State Disks (SSDs)), among others.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic, and should not constitute any limitation to the implementation process of the present application.
Additionally, the terms "system" and "network" are often used interchangeably herein. The term "and/or" herein is merely an association describing an associated object, meaning that three relationships may exist, e.g., a and/or B, may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
It should be understood that in this application, "B corresponding to A" means that B is associated with A, from which B can be determined. It should also be understood that determining B from a does not mean determining B from a alone, but may be determined from a and/or other information.
Those of ordinary skill in the art will appreciate that the elements and algorithm steps of the examples described in connection with the embodiments disclosed herein may be embodied in electronic hardware, computer software, or combinations of both, and that the components and steps of the examples have been described in a functional general in the foregoing description for the purpose of illustrating clearly the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In short, the above description is only a preferred embodiment of the present disclosure, and is not intended to limit the scope of the present disclosure. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A delivery quality inspection method of a direct-insertion LED lamp bead is characterized in that the method is applied to an intelligent delivery quality inspection system, the intelligent delivery quality inspection system is in communication connection with an image acquisition device and a temperature acquisition device, and the method comprises the following steps:
obtaining first historical detection statistical information, wherein the first historical detection statistical information is historical production statistical information of a first company;
obtaining second historical detection statistical information, wherein the second historical detection statistical information is historical production statistical information of a second company, and obtaining a first detection side weight distribution list according to the first historical detection statistical information and the second historical detection statistical information;
obtaining a first side weight compensation parameter set according to quality inspection product influence information, and correcting the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list;
acquiring data based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list to obtain a first image set and a first temperature acquisition set;
performing factory selective inspection test on the direct-insertion LED lamp beads based on the first image set and the first temperature acquisition set to obtain a first selective inspection result;
and performing factory pre-warning of the direct-insert LED lamp beads according to the first sampling inspection result.
2. The method of claim 1, wherein the method further comprises:
obtaining first quantity information of outgoing straight LED lamp beads;
obtaining first production batch information through production parameters of the LED lamp beads which are directly inserted;
determining a first sample sampling inspection constraint parameter according to the first quantity information and the first production batch information;
obtaining a first sampling inspection parameter based on the first sample sampling inspection constraint parameter and the second detection side weight distribution list;
obtaining the first set of images and a first set of temperature acquisitions based on the first spot check parameter.
3. The method of claim 2, wherein the method further comprises:
obtaining a first sampling product according to the first sampling parameter;
performing multi-angle image acquisition of the first sampled product by the image acquisition device to obtain the first image set;
constructing an abnormal feature identification list of the direct-insert LED lamp beads;
performing feature matching identification on the first image set based on the abnormal feature identification list to obtain a first matching identification result;
and obtaining the first sampling inspection result through the first matching identification result.
4. The method of claim 3, wherein the method further comprises:
acquiring bubble pictures of the direct-insert LED lamp beads through big data to obtain a first acquisition result;
carrying out artificial bubble grade identification on the first acquisition result to obtain a first identification result;
acquiring a sealing glue picture of the direct-insert LED lamp bead through big data to obtain a second acquisition result;
carrying out manual sealing grade identification on the second acquisition result to obtain a second identification result;
and constructing the abnormal feature recognition list through the first identification result and the second identification result.
5. The method of claim 4, wherein said performing feature matching recognition on said first set of images based on said list of anomalous feature recognitions further comprises:
performing feature matching identification on the first image set based on the abnormal feature identification list to obtain a first matching degree list of first matching features;
sequencing the first matching degree list in sequence to obtain a first sequencing result;
performing correlation analysis of adjacent matching degrees on the first matching degree in the first sequencing result to obtain a first correlation analysis result;
and performing matching degree distribution of the first sequencing result based on the first correlation analysis result, and obtaining a first matching identification result according to the matching degree distribution result.
6. The method of claim 5, wherein the method further comprises:
obtaining a first matching degree association preset threshold;
judging whether the first matching degree and the second matching degree meet a first matching degree association preset threshold value or not, wherein the second matching degree is the adjacent matching degree of the first matching degree in the first sequencing result;
and when the first matching degree and the second matching degree meet a preset threshold value associated with the first matching degree, obtaining the first matching identification result based on the first matching degree, the second matching degree and corresponding matching features.
7. The method of claim 1, wherein the method further comprises:
obtaining a first temperature control parameter;
sampling direct-insert LED lamp bead test is carried out through the first temperature control parameter, data acquisition is carried out through the temperature acquisition device, and a first temperature acquisition set is obtained.
8. The utility model provides a cut straightly system of examining of leaving factory of LED lamp pearl which characterized in that, the system includes:
a first obtaining unit, configured to obtain first historical detection statistical information, where the first historical detection statistical information is historical production statistical information of a first company;
a second obtaining unit, configured to obtain second historical detection statistical information, where the second historical detection statistical information is historical production statistical information of a second company, and obtain a first detection side weight distribution list according to the first historical detection statistical information and the second historical detection statistical information;
a third obtaining unit, configured to obtain a first side weight compensation parameter set according to quality inspection product influence information, and correct the first detection side weight distribution list based on the first side weight compensation parameter set to obtain a second detection side weight distribution list;
a fourth obtaining unit, configured to perform data acquisition based on the image acquisition device and the temperature acquisition device through the second detection side weight distribution list, to obtain a first image set and a first temperature acquisition set;
a fifth obtaining unit, configured to perform factory selective inspection testing on the in-line LED lamp beads based on the first image set and the first temperature acquisition set, and obtain a first selective inspection result;
and the first early warning unit is used for carrying out factory early warning on the direct-insert LED lamp beads according to the first sampling inspection result.
9. An electronic device comprising a processor and a memory; the memory is used for storing; the processor is used for executing the method of any one of claims 1 to 7 through calling.
10. A computer program product comprising a computer program and/or instructions, characterized in that the computer program and/or instructions, when executed by a processor, implement the steps of the method of any one of claims 1 to 7.
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| CN202211179342.5A CN115575322A (en) | 2022-09-27 | 2022-09-27 | Outgoing quality inspection method and system for direct-insertion LED lamp beads |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116342499A (en) * | 2023-02-27 | 2023-06-27 | 平湖市恒创旅游用品股份有限公司 | Multi-dimensional quality inspection method and system for bags |
| CN116757560A (en) * | 2023-08-22 | 2023-09-15 | 中国标准化研究院 | Intelligent quality inspection method for large data set data |
| CN117269826A (en) * | 2023-09-13 | 2023-12-22 | 深圳市柯瑞光电科技有限公司 | Automatic detection method and system for LED backlight source lamp bead production |
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2022
- 2022-09-27 CN CN202211179342.5A patent/CN115575322A/en active Pending
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116342499A (en) * | 2023-02-27 | 2023-06-27 | 平湖市恒创旅游用品股份有限公司 | Multi-dimensional quality inspection method and system for bags |
| CN116757560A (en) * | 2023-08-22 | 2023-09-15 | 中国标准化研究院 | Intelligent quality inspection method for large data set data |
| CN116757560B (en) * | 2023-08-22 | 2023-10-13 | 中国标准化研究院 | Intelligent quality inspection method for large data set data |
| CN117269826A (en) * | 2023-09-13 | 2023-12-22 | 深圳市柯瑞光电科技有限公司 | Automatic detection method and system for LED backlight source lamp bead production |
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