A method and system for detecting the packaging process of direct-insertion LED lamp beads

Abstract

The present invention discloses a method and system for detecting the packaging process of plug-in LED lamp beads. The method comprises: performing image acquisition on the packaging process of a target object to obtain first packaging image information, and then performing image extraction on the bonding process of the target object to obtain first bonding image information; performing temperature acquisition on the outer wall of a bonding container to obtain dynamic bonding temperature information; inputting the first bonding image information and the dynamic bonding temperature information into a material bonding evaluation model for training to generate a material bonding evaluation distribution result corresponding to any time point; performing image screening on bubbles accompanying the target object during the bonding process to obtain corresponding bubble distribution information at any time point, and based on this, performing data optimization on the material bonding evaluation distribution result to obtain a target bonding image corresponding to the target temperature; and facilitating comparative detection of subsequent packaging processes of the target object.

Description

Technical Field

[0001] The present invention relates to the field of data processing, and in particular to a method and system for detecting a packaging process of a plug-in LED lamp bead. Background Art

[0002] Direct-plug LEDs, also known as plug-in light-emitting diodes, are energy-saving and environmentally friendly visible light lighting materials. They are solid-state semiconductor devices that convert electrical energy into visible light. LEDs work by passing current through a compound semiconductor. Through the combination of electrons and holes, excess energy is released as light, creating a luminous effect. Common applications for direct-plug LEDs include household appliances, LED displays, outdoor signage, and automotive interior and exterior lighting.

[0003] Existing LED packaging mostly uses a potting process. The fundamental difficulties in process control are bubbles, missing materials, black spots, etc. Among them, the potting process is to first inject an adhesive into the LED molding cavity, then insert the press-welded LED bracket, put it into an oven to allow the adhesive to cure, and then remove the LED from the cavity to complete the molding.

[0004] However, in the existing process of potting lamp beads, it is difficult to accurately preheat the temperature of the adhesive, namely the resin and the hardener, resulting in continuous volatilization of the hardener during the bubbling process, making it difficult to degas, and affecting the technical problem of the potting effect of the lamp beads. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for detecting the packaging process of direct-insertion LED lamp beads, so as to solve the technical problem that it is difficult to accurately preheat the temperature of the adhesive, i.e., the resin, and the hardener during the existing potting process of the lamp beads, resulting in continuous volatilization of the hardener during the bubbling process, making it difficult to degas, and affecting the potting effect of the lamp beads.

[0006] In view of the above problems, the present invention provides a method and system for detecting the packaging process of direct-insertion LED lamp beads.

[0007] In a first aspect, the present invention provides a method for detecting the packaging process of plug-in LED lamp beads, characterized in that the method includes: based on a camera device, performing image acquisition on the packaging process of a target object to obtain first packaging image information; based on the first packaging image information, performing image extraction on the bonding process of the target object to obtain first bonding image information; based on a temperature sensor, performing temperature acquisition on the outer wall of the bonding container during the bonding process to obtain dynamic bonding temperature information; using the first bonding image information and the dynamic bonding temperature information as input information, inputting them into a material bonding evaluation model for training, and generating a material bonding evaluation distribution result corresponding to any time point of the bonding process; based on the first packaging image information, performing image screening on bubbles accompanying the target object in the bonding process to obtain the corresponding bubble distribution information at any time point; performing data optimization on the material bonding evaluation distribution result based on the bubble distribution information to obtain a target bonding image corresponding to the target temperature; and performing comparison detection on the subsequent packaging process of the target object based on the target temperature and the target bonding image.

[0008] On the other hand, the present invention also provides a direct-insertion LED lamp bead packaging process detection system for executing a direct-insertion LED lamp bead packaging process detection method as described in the first aspect, wherein the system comprises: a first acquisition unit, the first acquisition unit is used to perform image acquisition of the packaging process of the target object based on a camera device to obtain first packaging image information; a first extraction unit, the first extraction unit is used to perform image extraction of the bonding process of the target object based on the first packaging image information to obtain first bonding image information; a second acquisition unit, the second acquisition unit is used to perform temperature acquisition of the outer wall of the bonding container during the bonding process based on a temperature sensor to obtain dynamic bonding temperature information; a first input unit, the first input unit is used to input the first The bonding image information and the dynamic bonding temperature information are used as input information and input into the material bonding evaluation model for training to generate a material bonding evaluation distribution result corresponding to any time point of the bonding process; a first screening unit, the first screening unit is used to perform image screening on the bubbles accompanying the target object in the bonding process according to the first packaging image information, and obtain the corresponding bubble distribution information at any time point; a first optimization unit, the first optimization unit is used to perform data optimization on the material bonding evaluation distribution result based on the bubble distribution information, and obtain a target bonding image corresponding to the target temperature; a first detection unit, the first detection unit is used to compare and detect the subsequent packaging process of the target object according to the target temperature and the target bonding image.

[0009] In a third aspect, the present invention also provides a direct-insertion LED lamp bead packaging process detection system, comprising a memory, a processor, and a computer program stored in the memory and runnable on the processor, wherein the processor implements the steps of the method described in the first aspect when executing the program.

[0010] According to a fourth aspect, an electronic device includes a processor and a memory;

[0011] The memory is used for storing;

[0012] The processor is configured to execute, by calling, any one of the methods described in the first aspect.

[0013] In a fifth aspect, a computer program product comprises a computer program and / or instructions, which, when executed by a processor, implement the steps of the method described in any one of the first aspects.

[0014] One or more technical solutions provided in the present invention have at least the following technical effects or advantages:

[0015] The packaging process of the target object is imaged to obtain first packaging image information, and then the bonding process of the target object is imaged to obtain first bonding image information; the temperature of the outer wall of the bonding container is collected to obtain dynamic bonding temperature information; the first bonding image information and the dynamic bonding temperature information are input into the material bonding evaluation model for training to generate the material bonding evaluation distribution result corresponding to any time point; the bubbles accompanying the target object during the bonding process are image-screened to obtain the corresponding bubble distribution information at any time point, based on which data optimization is performed on the material bonding evaluation distribution result to obtain the target bonding image corresponding to the target temperature; this facilitates comparison and detection of the subsequent packaging process of the target object. By collecting images of the packaging process of the lamp beads, it is convenient to dynamically clarify the packaging effect of the lamp beads. At the same time, a temperature sensor is used to dynamically sense the temperature of the outer wall of the potting container, and then the acquired dynamic bonding image and dynamic temperature change are evaluated for the bonding at the corresponding time point. The bonding image corresponding to any temperature at any time point can be obtained. At the same time, the evaluation results are intervened and optimized according to the bubbles in the potting process shown in the image, and the preheating temperature under the desired state and its corresponding packaging image can be finally obtained for comparison and detection of the subsequent packaging process of the lamp beads, thereby achieving accurate temperature preheating of the lamp beads during the potting process, so that the bubbles that appear are promptly removed, thereby ensuring the technical effect of the potting effect of the LED lamp beads.

[0016] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are specifically listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the present invention or the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely exemplary, and a person of ordinary skill in the art can obtain other drawings based on the provided drawings without creative work.

[0018] Figure 1 This is a flow chart of a method for detecting the packaging process of a plug-in LED lamp bead according to the present invention;

[0019] Figure 2 This is a flow chart of generating a material adhesion evaluation distribution result corresponding to any time point of the adhesion process in a method for detecting the packaging process of a plug-in LED lamp bead according to the present invention;

[0020] Figure 3 This is a flow chart of comparative training based on the standard stirring viscosity as identification information in a method for detecting the packaging process of plug-in LED lamp beads according to the present invention;

[0021] Figure 4 A schematic diagram of a process for obtaining a target bonding image corresponding to a target temperature in a method for detecting a direct-insertion LED lamp bead packaging process according to the present invention;

[0022] Figure 5 This is a structural diagram of a direct-insertion LED lamp bead packaging process detection system of the present invention;

[0023] Figure 6 Schematic diagram of the structure of an exemplary electronic device of the present invention.

[0024] Description of reference numerals:

[0025] First acquisition unit 11, first extraction unit 12, second acquisition unit 13, first input unit 14, first screening unit 15, first optimization unit 16, first detection unit 17, bus 300, receiver 301, processor 302, transmitter 303, memory 304, bus interface 305. DETAILED DESCRIPTION

[0026] The present invention provides a method and system for detecting the packaging process of plug-in LED lamp beads, thereby solving the technical problem that it is difficult to accurately preheat the temperature of the adhesive, i.e., the resin, and the hardener, etc., during the existing potting process of the lamp beads, resulting in continuous volatilization of the hardener during the bubbling process, making it difficult to degas, and affecting the potting effect of the lamp beads. By collecting images of the lamp bead packaging process, it is convenient to dynamically clarify the packaging effect of the lamp bead. At the same time, a temperature sensor is used to dynamically sense the temperature of the outer wall of the potting container, and then the collected dynamic bonding image and dynamic temperature change are evaluated for bonding at corresponding time points. The bonding image corresponding to any temperature at any time point can be obtained. At the same time, the evaluation result is intervened and optimized according to the bubbles in the potting process displayed by the image, and the preheating temperature under the desired state and its corresponding packaging image can be finally obtained for comparison and detection of the subsequent packaging process of the lamp bead, thereby achieving the technical effect of accurately preheating the lamp bead potting process, so that the bubbles that appear are promptly removed, thereby ensuring the potting effect of the LED lamp bead.

[0027] The acquisition, storage, use, and processing of data in the technical solution of the present invention are in compliance with the relevant provisions of national laws and regulations.

[0028] Below, the technical solutions of the present invention will be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments described herein. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. It should also be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the accompanying drawings.

[0029] The present invention provides a method for detecting the packaging process of plug-in LED lamp beads, characterized in that the method includes: based on a camera device, performing image acquisition on the packaging process of a target object to obtain first packaging image information; based on the first packaging image information, performing image extraction on the bonding process of the target object to obtain first bonding image information; based on a temperature sensor, performing temperature acquisition on the outer wall of a bonding container during the bonding process to obtain dynamic bonding temperature information; using the first bonding image information and the dynamic bonding temperature information as input information, inputting them into a material bonding evaluation model for training, and generating a material bonding evaluation distribution result corresponding to any time point of the bonding process; based on the first packaging image information, performing image screening on bubbles accompanying the target object during the bonding process to obtain the corresponding bubble distribution information at any time point; performing data optimization on the material bonding evaluation distribution result based on the bubble distribution information to obtain a target bonding image corresponding to a target temperature; and performing comparative detection on a subsequent packaging process of the target object based on the target temperature and the target bonding image.

[0030] After introducing the basic principles of the present invention, various non-limiting embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0031] Example 1

[0032] Please see the attached Figure 1 The present invention provides a method for detecting the packaging process of a plug-in LED lamp bead, wherein the method is applied to a system for detecting the packaging process of a plug-in LED lamp bead, and the method specifically comprises the following steps:

[0033] Step S100: Capturing images of the packaging process of the target object using a camera device to obtain first packaging image information;

[0034] Step S200: extracting an image of the bonding process of the target object according to the first packaging image information to obtain first bonding image information;

[0035] Specifically, a plug-in LED (DIP LED), also known as a plug-in light-emitting diode (LED), is a type of energy-saving and environmentally friendly visible light lighting material. It is a solid-state semiconductor device that converts electrical energy into visible light. LEDs work by passing an electric current through a compound semiconductor. Through the combination of electrons and holes, the excess energy is released as light, creating the luminous effect. Common applications for DIP LEDs include household appliances, LED displays, outdoor billboards, and automotive interior and exterior lighting.

[0036] Existing LED packaging mostly uses a potting process. The fundamental difficulties in process control are bubbles, missing materials, black spots, etc. Among them, the potting process is to first inject an adhesive into the LED molding cavity, then insert the press-welded LED bracket, put it into an oven to allow the adhesive to cure, and then remove the LED from the cavity to complete the molding.

[0037] However, in the existing process of potting lamp beads, it is difficult to accurately preheat the temperature of the adhesive, namely the resin and the hardener, resulting in continuous volatilization of the hardener during the bubbling process, making it difficult to degas, and affecting the potting effect of the lamp beads.

[0038] In order to solve the above problems, the present application proposes a method for detecting the packaging process of direct-insertion LED lamp beads. By collecting images of the packaging process of the lamp beads, it is convenient to dynamically clarify the packaging effect of the lamp beads. At the same time, a temperature sensor is used to dynamically sense the outer wall temperature of the potting container, and then the collected dynamic bonding image and dynamic temperature change are evaluated for the bonding at the corresponding time point. The bonding image corresponding to any temperature at any time point can be obtained. At the same time, the evaluation results are intervened and optimized according to the bubbles in the potting process shown in the image, and the preheating temperature under the expected state and its corresponding packaging image can be finally obtained for comparison and detection of the subsequent packaging process of the lamp beads, thereby achieving the technical effect of accurate temperature preheating of the potting process of the lamp beads, so that the bubbles that appear are promptly removed, thereby ensuring the potting effect of the LED lamp beads.

[0039] Specifically, the target object is an LED lamp bead, and the first packaging image information is the image acquisition of the entire packaging process of the LED lamp bead, including potting, film pressing, demolding, etc. The first bonding image information is only to extract the image of the potting process separately, so as to separately study the influence of temperature control in the potting process on the potting effect. The viscosity of the material in the potting process can be effectively analyzed. If the mixed viscosity is high, it means that the preheating temperature is low.

[0040] Step S300: using a temperature sensor to collect the temperature of the outer wall of the bonding container during the bonding process to obtain dynamic bonding temperature information;

[0041] Step S400: inputting the first bonding image information and the dynamic bonding temperature information as input information into a material bonding assessment model for training, thereby generating a material bonding assessment distribution result corresponding to any time point of the bonding process;

[0042] Further, such as Figure 2 As shown, step S400 includes:

[0043] Step S410: obtaining a first frame of glued image, a second frame of glued image, and finally an Nth frame of glued image in a forward time sequence according to the first glued image information;

[0044] Step S420: obtaining a first bonding temperature, a second bonding temperature, and finally an Nth bonding temperature in the forward time series according to the dynamic bonding temperature information;

[0045] Step S430: inputting the first bonding image and the first bonding temperature, the second bonding image and the second bonding temperature, and so on, up to the Nth bonding image and the Nth bonding temperature, into the material bonding assessment model for training;

[0046] Step S440: sequentially obtaining training results of the material adhesion evaluation model, wherein the training results include a first adhesion evaluation result, a second adhesion evaluation result, and finally an Nth adhesion evaluation result.

[0047] Among them, Figure 3 As shown, step S430 includes:

[0048] Step S431: Based on the material adhesion evaluation model, feature fusion is performed on the first frame adhesion image and the first adhesion temperature to obtain a first viscosity of the target object;

[0049] Step S432: performing feature fusion on the second bonding image and the second bonding temperature to obtain a second viscosity of the target object, up to an Nth viscosity;

[0050] Step S433: presetting a standard stirring viscosity of the target object during the bonding process;

[0051] Step S434: constructing a viscosity comparison model of the bonding process based on the standard stirring viscosity;

[0052] Step S435: sequentially inputting the first viscosity, the second viscosity, and finally the Nth viscosity into the viscosity comparison model, performing comparison training based on the standard stirring viscosity as identification information, and generating the material adhesion evaluation distribution result.

[0053] Specifically, after capturing images of the lamp bead packaging process, a temperature sensor can also be used to dynamically sense the temperature of the outer wall of the potting container, so as to effectively capture the preheating temperature at any time point. The dynamic bonding temperature information is the preheating temperature corresponding to any time point obtained, wherein any time point corresponds one-to-one to the time point of the above-mentioned image capture process, that is, one time point corresponds to one preheating temperature and one potting image.

[0054] Furthermore, the first bonding image information and the dynamic bonding temperature information are used as input information and input into the material bonding evaluation model for training, wherein the material bonding evaluation model is used to effectively evaluate and analyze any preheating temperature and its corresponding potting image at a single time point. Specifically, the first bonding image information is split into a first frame bonding image, a second frame bonding image, and an Nth frame bonding image with a positive time sequence, wherein the positive time sequence is the positive time development from the start of potting to the end of potting, and the first frame bonding image is the image information of the first time point, i.e., the start of potting, and the subsequent bonding images are deduced accordingly. At the same time, the first bonding temperature, the second bonding temperature, and the Nth bonding temperature are obtained in the positive time sequence, and the first bonding temperature corresponds to the first frame bonding image, and the subsequent bonding temperatures are deduced accordingly. Finally, the first frame bonding image and the first bonding temperature, the second frame bonding image and the second bonding temperature, up to the N-th frame bonding image and the N-th bonding temperature, are sequentially input into the material adhesion evaluation model for training, so as to obtain the training results of the material adhesion evaluation model, specifically including the first adhesion evaluation result, the second adhesion evaluation result, up to the N-th adhesion evaluation result, wherein each adhesion evaluation result can be compared and analyzed by the viscosity of the material during the potting process.

[0055] After obtaining the bonding temperature and its potting image corresponding to any time point, feature fusion can be performed on them. For example, the first frame bonding image and the first bonding temperature are feature fused to obtain the first viscosity of the target object. The first viscosity can be characterized by the effect of the first bonding temperature preheating on the potting bonding image of the lamp bead. At the beginning, the preheating temperature is low, and the adhesive cannot melt quickly, so that the spatial distance between various molecules in the adhesive is close and cannot move quickly, resulting in a relatively high mixed viscosity. Then, the second frame bonding image and the second bonding temperature are feature fused to obtain the second viscosity of the target object, until the Nth viscosity, the second The viscosity is lower than the first viscosity because the preheating temperature gradually increases, and so on, until the viscosity of the adhesive during the entire preheating process is collected. At the same time, the standard stirring viscosity of the target object during the bonding process can be preset. The standard stirring viscosity is the preset state at a certain preheating temperature, which makes the molecular movement in the adhesive reach the most active state. At this time, the viscosity is reduced, so that the inserted lamp beads remain in a stable state, thereby obtaining rapid potting. By constructing a good viscosity comparison model, the standard stirring viscosity is used as identification information, and the above-mentioned viscosities can be compared and trained to facilitate the specific generation of the material adhesion evaluation distribution results.

[0056] Step S500: performing image screening on bubbles accompanying the target object during the bonding process according to the first packaging image information to obtain corresponding bubble distribution information at any time point;

[0057] Step S600: performing data optimization on the material adhesion evaluation distribution result based on the bubble distribution information to obtain a target adhesion image corresponding to a target temperature;

[0058] Further, such as Figure 4 As shown, step S600 includes:

[0059] Step S610: Presetting a first data optimization feature and a second data optimization feature according to the bubble distribution information;

[0060] Step S620: Building a first-level optimization layer based on the first data optimization feature, and building a second-level optimization layer based on the second data optimization feature;

[0061] Step S630: uploading the material adhesion evaluation distribution result to the first-level optimization layer for data optimization to obtain a primary material adhesion evaluation optimization result;

[0062] Step S640: uploading the primary material adhesion evaluation optimization result to the secondary optimization layer for data optimization to obtain the desired material adhesion evaluation optimization result;

[0063] Step S650: performing graphic conversion on the optimization result of the adhesion evaluation of the desired substance to obtain a temperature change-adhesion strength statistical graph of the target object;

[0064] Step S660: traversing and analyzing the temperature change-adhesion strength statistical graph to obtain the adhesion strength valley point;

[0065] Step S670: Obtaining the target temperature and the target bonding image corresponding to the bonding strength valley point;

[0066] Furthermore, step S600 further includes:

[0067] Step S680: If there are P bonding strength valley points in the temperature change-bonding strength statistical graph, obtain P temperature distribution information corresponding to the P bonding strength valley points, where P is a positive integer greater than or equal to 2;

[0068] Step S690: performing a directed traversal on the P temperature distribution information to obtain the target temperature, wherein the target temperature tends to be stable within a preset time.

[0069] Specifically, during the potting process of the lamp beads, bubbles and other phenomena often occur due to insufficient preheating temperature and irregular stirring. The appearance of bubbles greatly affects the potting effect of the lamp beads. By performing image screening on the bubbles that appear during the potting process, the bubble distribution information can be obtained, wherein the bubble distribution information still corresponds one-to-one with any of the above-mentioned time points. As the preheating temperature gradually increases, the bubbles that appear during the potting process can be quickly degassed through means such as uniform stirring, thereby ensuring the potting effect of the lamp beads.

[0070] Because the aforementioned material adhesion assessment distribution results do not account for the impact of bubbles, the bubble distribution information can be used to optimize the material adhesion assessment distribution results to obtain a target adhesion image corresponding to the target temperature. Specifically, based on the bubble distribution information, a first data optimization feature and a second data optimization feature can be preset. The first data optimization feature can be understood as bubble size, and the second data optimization feature can be understood as bubble distribution density. Data optimization can be performed based on these two dimensions.

[0071] Specifically, a first-level optimization layer can be constructed based on the first data optimization feature, wherein the first-level optimization layer is used to perform primary optimization of the material adhesion evaluation distribution result according to the size of the bubbles appearing during potting, and the primary material adhesion evaluation optimization result is the optimized result. At the same time, a second-level optimization layer is constructed based on the second data optimization feature, wherein the second-level optimization layer is used to perform secondary optimization of the primary material adhesion evaluation optimization result according to the bubble distribution density appearing during potting, and the expected material adhesion evaluation optimization result is the result obtained after two feature optimizations, which characterizes the evaluation result obtained after optimizing the initial material adhesion evaluation distribution result according to the bubble size and distribution density appearing during potting, making the data more convincing and making the control of the preheating temperature and potting effect during the potting process more precise.

[0072] Since the optimized evaluation results are displayed through a large number of images, in order to accurately display the optimized evaluation results, they can be converted into graphics, that is, the potting effects displayed in each image in the massive images are labeled, and then each labeled label is displayed in a statistical chart, which can be specifically displayed through a line chart, etc. The line chart reflects the bonding strength in the potting container corresponding to each preheating temperature point in the potting process, that is, it is converted into a temperature change-bonding strength statistical chart of the lamp beads. By traversing the image, the valley point in the line chart can be obtained, that is, the point with the lowest bonding strength corresponding to a certain preheating temperature, and it also represents that at the preheating temperature, the LED lamp beads can be quickly fixed and potted. Finally, the target temperature and the target bonding image corresponding to the bonding strength valley point are obtained, and the subsequent potting process can be compared and detected based on the target bonding image.

[0073] If poor curing results in multiple (at least two) bond strength valleys in the line graph, preheating temperature distribution information corresponding to the valleys can be obtained. The target temperature and target bonding profile can be determined by selecting the preheating temperature that stabilizes within a preset time from the preheating temperature distribution information. The preset time is determined based on the actual potting process.

[0074] Step S700: performing a comparison and detection on a subsequent packaging process of the target object according to the target temperature and the target bonding image.

[0075] Furthermore, step S700 includes:

[0076] Step S710: Capturing images of the subsequent packaging process of the target object to obtain subsequent packaging images;

[0077] Step S720: Presetting a preheating temperature warning value for the subsequent packaging process based on the target temperature;

[0078] Step S730: Presetting a bonding strength warning value for the subsequent packaging process based on the target bonding image;

[0079] Step S740: performing a comparison test on a subsequent packaging process of the target object according to the preheating temperature warning value and the bonding strength warning value.

[0080] Specifically, when comparing and detecting the subsequent packaging process of the target object, specifically, image capture can be performed on the subsequent packaging process of the target object to obtain a subsequent packaging image, wherein the subsequent packaging image is an image capture of the packaging of other LED lamp beads in the batch. Based on this, according to the target temperature, the preheating temperature warning value of the subsequent packaging process is preset. For example, the target temperature is defined as 50℃-80℃, and the preheating temperature warning value can be characterized as a lower temperature of the handover temperature of 50℃ within the above temperature value range, which can be 45℃, etc., and then heating is performed through residual heat so that the final preheating temperature reaches 50℃, or 65℃, etc. After the adhesive is heated to a certain temperature value, heating is stopped so that the residual temperature is heated to 80℃, so that it does not exceed 80℃.

[0081] At the same time, based on the target bonding image, a bonding strength warning value for the subsequent packaging process is preset. The target bonding image is a captured image of the stable potting of the lamp beads. Based on this, the bonding strength warning value is preset. The bonding strength warning values ​​correspond to the potting image matching 45°C and the potting image corresponding to 65°C. Furthermore, the preheating temperature warning value and the bonding strength warning value are used to compare and detect the subsequent packaging process of the target object.

[0082] In summary, the present invention provides a method for detecting the packaging process of a plug-in LED lamp bead, which has the following technical effects:

[0083] 1. Capture images of the target object during the packaging process to obtain first packaging image information, and then extract images of the target object during the bonding process to obtain first bonding image information; collect the temperature of the outer wall of the bonding container to obtain dynamic bonding temperature information; input the first bonding image information and dynamic bonding temperature information into the material bonding assessment model for training to generate a material bonding assessment distribution result corresponding to any time point; perform image screening on bubbles accompanying the target object during the bonding process to obtain the corresponding bubble distribution information at any time point, and based on this, perform data optimization on the material bonding assessment distribution result to obtain a target bonding image corresponding to the target temperature; this facilitates comparison and detection of the subsequent packaging process of the target object. By collecting images of the packaging process of the lamp beads, it is convenient to dynamically clarify the packaging effect of the lamp beads. At the same time, a temperature sensor is used to dynamically sense the temperature of the outer wall of the potting container, and then the acquired dynamic bonding image and dynamic temperature change are evaluated for the bonding at the corresponding time point. The bonding image corresponding to any temperature at any time point can be obtained. At the same time, the evaluation results are intervened and optimized according to the bubbles in the potting process shown in the image, and the preheating temperature under the desired state and its corresponding packaging image can be finally obtained for comparison and detection of the subsequent packaging process of the lamp beads, thereby achieving accurate temperature preheating of the lamp beads during the potting process, so that the bubbles that appear are promptly removed, thereby ensuring the technical effect of the potting effect of the LED lamp beads.

[0084] 2. By screening the bubbles that appear during the potting process through image analysis, we can obtain bubble distribution information. As the preheating temperature gradually increases, we can quickly remove the bubbles that appear during the potting process through means such as uniform stirring, thereby ensuring the potting effect of the lamp beads. The evaluation results obtained after optimizing the initial material adhesion evaluation distribution results based on the size and distribution density of the bubbles that appear during potting make the data more convincing and enable more precise control of the preheating temperature and potting effect during the potting process.

[0085] Example 2

[0086] Based on the same invention concept as the method for detecting the packaging process of a plug-in LED lamp bead in the aforementioned embodiment, the present invention also provides a system for detecting the packaging process of a plug-in LED lamp bead, see the attached Figure 5 , the system comprising:

[0087] A first acquisition unit 11 is configured to acquire images of the packaging process of the target object using a camera device to obtain first packaging image information;

[0088] a first extraction unit 12, configured to extract an image of the bonding process of the target object according to the first packaging image information to obtain first bonding image information;

[0089] A second collecting unit 13 is used to collect the temperature of the outer wall of the bonding container during the bonding process using a temperature sensor to obtain dynamic bonding temperature information;

[0090] A first input unit 14 is configured to input the first bonding image information and the dynamic bonding temperature information as input information into a material bonding assessment model for training, thereby generating a material bonding assessment distribution result corresponding to any time point of the bonding process;

[0091] a first screening unit 15, configured to perform image screening of bubbles accompanying the target object during the bonding process based on the first packaging image information, and obtain corresponding bubble distribution information at any time point;

[0092] A first optimization unit 16 is configured to perform data optimization on the material adhesion evaluation distribution result based on the bubble distribution information to obtain a target adhesion image corresponding to a target temperature;

[0093] The first detection unit 17 is used to perform a comparison detection on a subsequent packaging process of the target object according to the target temperature and the target bonding image.

[0094] Furthermore, the system further comprises:

[0095] a first obtaining unit, configured to obtain, according to the first glued image information, a first glued image frame, a second glued image frame, and finally an Nth glued image frame in a forward time sequence;

[0096] a second obtaining unit, configured to obtain a first bonding temperature, a second bonding temperature, and finally an Nth bonding temperature in the forward time series according to the dynamic bonding temperature information;

[0097] a second input unit, configured to sequentially input the first bonding image and the first bonding temperature, the second bonding image and the second bonding temperature, and finally the N-th bonding image and the N-th bonding temperature, into the material bonding assessment model for training;

[0098] A third obtaining unit is used to sequentially obtain training results of the material adhesion evaluation model, wherein the training results include a first adhesion evaluation result, a second adhesion evaluation result, and an Nth adhesion evaluation result.

[0099] Furthermore, the system further comprises:

[0100] a first fusion unit configured to perform feature fusion on the first frame adhesion image and the first adhesion temperature based on the material adhesion evaluation model to obtain a first viscosity of the target object;

[0101] a second fusion unit configured to perform feature fusion on the second bonding image frame and the second bonding temperature to obtain a second viscosity of the target object, up to an Nth viscosity;

[0102] a first preset unit, the first preset unit being used to preset a standard stirring viscosity of the target object during the bonding process;

[0103] a first construction unit, configured to construct a viscosity comparison model of the bonding process based on the standard stirring viscosity;

[0104] A third input unit is used to sequentially input the first viscosity, the second viscosity, and finally the Nth viscosity into the viscosity comparison model, perform comparison training based on the standard stirring viscosity as identification information, and generate the material adhesion evaluation distribution result.

[0105] Furthermore, the system further comprises:

[0106] a second preset unit, configured to preset a first data optimization feature and a second data optimization feature according to the bubble distribution information;

[0107] a first building unit, configured to build a first-level optimization layer based on the first data optimization feature, and to build a second-level optimization layer based on the second data optimization feature;

[0108] a first uploading unit, configured to upload the material adhesion evaluation distribution result to the first-level optimization layer for data optimization to obtain a primary material adhesion evaluation optimization result;

[0109] The second uploading unit is used to upload the primary material adhesion evaluation optimization result to the secondary optimization layer for data optimization to obtain the desired material adhesion evaluation optimization result.

[0110] Furthermore, the system further comprises:

[0111] a first conversion unit, configured to perform graphical conversion on the desired material adhesion evaluation optimization result to obtain a temperature change-adhesion strength statistical graph of the target object;

[0112] a first parsing unit, configured to traverse and parse the temperature change-adhesion strength statistical graph to obtain a valley point of the adhesion strength;

[0113] A fourth obtaining unit is configured to obtain the target temperature and the target bonding image corresponding to the bonding strength valley point.

[0114] Furthermore, the system further comprises:

[0115] A fifth obtaining unit, configured to obtain P pieces of temperature distribution information corresponding to the P bonding strength valley points if there are P bonding strength valley points in the temperature change-bonding strength statistical graph, where P is a positive integer greater than or equal to 2;

[0116] A sixth obtaining unit is configured to perform a directed traversal on the P pieces of temperature distribution information to obtain the target temperature, wherein the target temperature tends to be stable within a preset time.

[0117] Furthermore, the system further comprises:

[0118] a third acquisition unit, configured to acquire images of the subsequent packaging process of the target object to obtain subsequent packaging images;

[0119] a third preset unit, configured to preset a preheating temperature warning value for the subsequent packaging process based on the target temperature;

[0120] a fourth preset unit, configured to preset a bonding strength warning value for a subsequent packaging process based on the target bonding image;

[0121] The second detection unit is used to compare and detect the subsequent packaging process of the target object according to the preheating temperature warning value and the bonding strength warning value.

[0122] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. Figure 1The method and specific examples of detecting the packaging process of a plug-in LED lamp bead in the first embodiment are also applicable to the system for detecting the packaging process of a plug-in LED lamp bead in the present embodiment. Through the detailed description of the method for detecting the packaging process of a plug-in LED lamp bead, those skilled in the art can clearly understand the system for detecting the packaging process of a plug-in LED lamp bead in the present embodiment. Therefore, for the sake of brevity, the detailed description is not repeated here. As for the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple. For relevant details, please refer to the method description.

[0123] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

[0124] Exemplary electronic devices

[0125] Reference below Figure 6 The electronic device of the present invention will be described.

[0126] Figure 6 FIG2 shows a schematic structural diagram of an electronic device according to the present invention.

[0127] Based on the inventive concept of a direct-plug LED lamp bead packaging process detection method in the aforementioned embodiment, the present invention also provides a direct-plug LED lamp bead packaging process detection system, which stores a computer program. When the program is executed by a processor, it implements the steps of any method of the direct-plug LED lamp bead packaging process detection method described above.

[0128] Among them, Figure 6 In the embodiment of the present invention, a bus architecture (represented by bus 300) is shown. Bus 300 may include any number of interconnected buses and bridges. Bus 300 links various circuits together, including one or more processors represented by processor 302 and memory represented by memory 304. Bus 300 may also link various other circuits together, such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art and are therefore not described further herein. Bus interface 305 provides an interface between bus 300 and receiver 301 and transmitter 303. Receiver 301 and transmitter 303 may be the same component, namely a transceiver, which provides a means for communicating with various other devices over a transmission medium.

[0129] The processor 302 is responsible for managing the bus 300 and general processing, while the memory 304 may be used to store data used by the processor 302 when performing operations.

[0130] The present invention provides a method for detecting the packaging process of plug-in LED lamp beads, characterized in that the method includes: based on a camera device, performing image acquisition on the packaging process of a target object to obtain first packaging image information; based on the first packaging image information, performing image extraction on the bonding process of the target object to obtain first bonding image information; based on a temperature sensor, performing temperature acquisition on the outer wall of a bonding container during the bonding process to obtain dynamic bonding temperature information; using the first bonding image information and the dynamic bonding temperature information as input information, inputting them into a material bonding evaluation model for training, and generating a material bonding evaluation distribution result corresponding to any time point of the bonding process; based on the first packaging image information, performing image screening on bubbles accompanying the target object during the bonding process to obtain the corresponding bubble distribution information at any time point; performing data optimization on the material bonding evaluation distribution result based on the bubble distribution information to obtain a target bonding image corresponding to a target temperature; and performing comparative detection on a subsequent packaging process of the target object based on the target temperature and the target bonding image. The invention solves the technical problem that it is difficult to accurately preheat the temperature of the adhesive, i.e. the resin and the hardener, etc. during the existing potting process of the lamp beads, which causes the hardener to continue to volatilize during the bubbling process, making it difficult to degas, and affecting the potting effect of the lamp beads. By collecting images of the packaging process of the lamp beads, it is convenient to dynamically clarify the packaging effect of the lamp beads. At the same time, a temperature sensor is used to dynamically sense the temperature of the outer wall of the potting container, and then the collected dynamic bonding image and dynamic temperature change are evaluated for the bonding at the corresponding time point. The bonding image corresponding to any temperature at any time point can be obtained. At the same time, the evaluation result is intervened and optimized according to the bubbles in the potting process shown in the image, and the preheating temperature under the desired state and its corresponding packaging image can be finally obtained for comparison and detection of the subsequent packaging process of the lamp beads, thereby achieving the technical effect of accurate temperature preheating of the lamp beads during the potting process, so that the bubbles that appear are promptly removed, thereby ensuring the potting effect of the LED lamp beads.

[0131] The present invention also provides an electronic device, comprising a processor and a memory;

[0132] The memory is used for storing;

[0133] The processor is used to execute the method described in any one of the above-mentioned embodiments by calling.

[0134] The present invention also provides a computer program product, including a computer program and / or instructions, which, when executed by a processor, implement the steps of the method described in any one of the above-mentioned embodiments.

[0135] Those skilled in the art will appreciate that the embodiments of the present invention may be provided as methods, devices, or computer program products. Therefore, the present invention may take the form of a complete software embodiment, a complete hardware embodiment, or a combination of software and hardware embodiments. In addition, the present invention is in the form of a computer program product that can be implemented on one or more computer-usable storage media containing computer-usable program code. The computer-usable storage medium includes, but is not limited to, various media that can store program code, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk storage, a compact disc read-only memory (CD-ROM), and an optical storage device.

[0136] The present invention is described with reference to the flowcharts and / or block diagrams of the methods, apparatus (systems), and computer program products of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A system that specifies the functions of a box or boxes.

[0137] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including an instruction system that is implemented in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0138] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1Although preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they understand the basic creative concepts.

[0139] Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the present invention and its equivalents, the present invention is intended to include these modifications and variations.

Claims

1. A method for detecting the packaging process of a plug-in LED lamp bead, characterized in that: The method comprises: Based on the camera device, an image of the packaging process of the target object is collected to obtain first packaging image information; extracting an image of the bonding process of the target object according to the first packaging image information to obtain first bonding image information; Using a temperature sensor, the temperature of the outer wall of the bonding container during the bonding process is collected to obtain dynamic bonding temperature information; The first bonding image information and the dynamic bonding temperature information are input into a material bonding assessment model for training to generate a material bonding assessment distribution result corresponding to any time point of the bonding process; performing image screening of bubbles accompanying the target object during the bonding process according to the first packaging image information to obtain corresponding bubble distribution information at any time point; performing data optimization on the material adhesion evaluation distribution result based on the bubble distribution information to obtain a target adhesion image corresponding to a target temperature; performing a comparison inspection on a subsequent packaging process of the target object according to the target temperature and the target bonding image; According to the first glued image information, a first glued image frame, a second glued image frame, and finally an Nth glued image frame are obtained in a forward time sequence; According to the dynamic bonding temperature information, obtaining a first bonding temperature, a second bonding temperature, and finally an Nth bonding temperature in the forward time series; inputting the first bonding image and the first bonding temperature, the second bonding image and the second bonding temperature, and finally the N-th bonding image and the N-th bonding temperature into the material bonding assessment model for training; Sequentially obtaining training results of the material adhesion evaluation model, the training results including a first adhesion evaluation result, a second adhesion evaluation result, and so on to an Nth adhesion evaluation result; Based on the material adhesion evaluation model, feature fusion is performed on the first adhesion image and the first adhesion temperature to obtain a first viscosity of the target object; Performing feature fusion on the second bonding image and the second bonding temperature to obtain a second viscosity of the target object, up to an Nth viscosity; Presetting a standard stirring viscosity of the target object during the bonding process; Based on the standard stirring viscosity, constructing a viscosity comparison model of the bonding process; sequentially inputting the first viscosity, the second viscosity, and finally the Nth viscosity into the viscosity comparison model, performing comparison training based on the standard stirring viscosity as identification information, and generating the material adhesion evaluation distribution result; Presetting a first data optimization feature and a second data optimization feature according to the bubble distribution information; Based on the first data optimization feature, a first-level optimization layer is built, and based on the second data optimization feature, a second-level optimization layer is built; Uploading the material adhesion evaluation distribution result to the first-level optimization layer for data optimization to obtain a primary material adhesion evaluation optimization result; The primary material adhesion evaluation optimization result is uploaded to the secondary optimization layer for data optimization to obtain the desired material adhesion evaluation optimization result.

2. The method according to claim 1, wherein The method comprises: Performing graphic conversion on the optimization result of the adhesion evaluation of the desired substance to obtain a temperature change-adhesion strength statistical graph of the target object; Performing traversal analysis on the temperature change-bonding strength statistical graph to obtain bonding strength valley points; The target temperature and the target bonding image corresponding to the bonding strength valley point are obtained.

3. The method according to claim 2, wherein The method comprises: If there are P bonding strength valley points in the temperature change-bonding strength statistical graph, obtain P temperature distribution information corresponding to the P bonding strength valley points, where P is a positive integer greater than or equal to 2; Directed traversal is performed on the P temperature distribution information to obtain the target temperature, wherein the target temperature tends to be stable within a preset time.

4. The method according to claim 3, wherein The method comprises: Capturing images of the subsequent packaging process of the target object to obtain subsequent packaging images; Based on the target temperature, presetting a preheating temperature warning value for the subsequent packaging process; Presetting a bonding strength warning value for the subsequent packaging process based on the target bonding image; A comparison test is performed on a subsequent packaging process of the target object according to the preheating temperature warning value and the bonding strength warning value.

5. A direct-insertion LED lamp bead packaging process detection system, characterized in that: The system is used to perform the method according to any one of claims 1 to 4, and the system comprises: a first acquisition unit, configured to acquire images of a packaging process of a target object based on a camera device to obtain first packaging image information; a first extraction unit, configured to extract an image of the bonding process of the target object according to the first packaging image information to obtain first bonding image information; a second collecting unit, configured to collect the temperature of the outer wall of the bonding container during the bonding process using a temperature sensor to obtain dynamic bonding temperature information; a first input unit, configured to input the first bonding image information and the dynamic bonding temperature information as input information into a material bonding assessment model for training, and generate a material bonding assessment distribution result corresponding to any time point of the bonding process; a first screening unit, configured to perform image screening on bubbles accompanying the target object during the bonding process based on the first packaging image information, and obtain corresponding bubble distribution information at any time point; a first optimization unit, configured to perform data optimization on the material adhesion evaluation distribution result based on the bubble distribution information to obtain a target adhesion image corresponding to a target temperature; A first detection unit is used to perform a comparison detection on a subsequent packaging process of the target object according to the target temperature and the target bonding image.

6. An electronic device, characterized in that: including processor and memory; The memory is used for storing; The processor is configured to execute the method according to any one of claims 1 to 4 by calling.

7. A computer program product comprising a computer program and / or instructions, characterized in that When the computer program and / or instructions are executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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