Machine vision-based toothbrush finished product and packaging defect detection system

Abstract

This invention relates to the field of visual inspection technology, and more particularly to a machine vision-based defect detection system for finished toothbrushes and their packaging. The system includes a toothbrush defect detection center, an image processing unit, an image analysis unit, a defect factor acquisition unit, a production line planning unit, and a visual feedback unit. Based on model-based analysis, this invention quickly identifies whether surface images of collected finished toothbrush products and packaging contain defects. Furthermore, given the presence of defects, it summarizes the concentrated distribution periods of defects in the finished toothbrush products and packaging to quickly identify frequent defect intervals in the toothbrush production line. Simultaneously, it displays the type distribution of defects within these frequent defect intervals and analyzes the factors contributing to defects in these intervals. Specifically, by analyzing and identifying interference factors from related equipment, it determines whether defects primarily occur in the processing or packaging areas of the toothbrush production line, thereby improving the targeting and effectiveness of zoned control.

Description

Technical Field

[0001] This invention relates to the field of visual inspection technology, and in particular to a machine vision-based system for detecting defects in finished toothbrush products and their packaging. Background Technology

[0002] During the toothbrush production process, various defects may occur in the finished toothbrush and its packaging, such as missing, bent, or deformed bristles, as well as cracked or color-different toothbrush handles. Common manual inspection methods have inconsistent inspection standards and are prone to missed inspections, making them difficult to meet the needs of modern production under today's large-scale demand.

[0003] With the continuous development of machine vision technology, its application to defect detection in finished toothbrush products and packaging has significant practical implications. However, current machine vision technology still struggles to analyze the time distribution and type distribution of defects when detecting defects in finished toothbrush products and packaging. Consequently, it is impossible to target and manage related terminal devices, resulting in a persistently high defect rate. Furthermore, it is difficult to implement zoned control of the production line based on defect distribution to reduce the frequency of defects. Therefore, a solution is proposed. Summary of the Invention

[0004] The purpose of this invention is to provide a machine vision-based defect detection system for finished toothbrush products and packaging. It mainly analyzes, processes, and locks down defects based on the distribution of defect occurrence times and defect types, and manages them in a targeted manner. It also uses the defect distribution to implement zoned control of the production line, thereby solving the technical defects mentioned in the background art.

[0005] The objective of this invention can be achieved through the following technical solution: a toothbrush finished product and packaging defect detection system based on machine vision, including a toothbrush defect detection center, an image processing unit, an image analysis unit, a defect factor acquisition unit, a production line planning unit, and a visual feedback unit;

[0006] Toothbrush Defect Detection Center: Used to retrieve multi-faceted feature images of finished toothbrushes and their packaging collected by multiple sets of industrial cameras and light source equipment on the toothbrush production line;

[0007] Image processing unit: used to preprocess the multi-faceted feature image set of the collected toothbrush products and packaging;

[0008] Image analysis unit: used to perform defect identification and distribution analysis on the preprocessed multi-faceted feature image set, to determine whether there are defects in the finished toothbrush and packaging, and to perform discrimination processing on the defect type of each A obtained to obtain a single type interval or multiple type intervals;

[0009] Defect Factor Acquisition Unit: Used to perform multi-angle analysis of the factors causing defects in related equipment, determine whether the occurrence of a single type of interval is caused by internal or external factors of the related equipment, and obtain external normal signals or suspicious interference items or interfering equipment or conventional equipment.

[0010] Production line planning unit: Used for zoned control and cycle planning analysis of the processing and packaging areas of the toothbrush production line, to determine whether the defects in the toothbrush production line mainly occur in the processing area or the packaging area, and to obtain production line management signals, single management signals or processing routine signals.

[0011] Preferably, the multi-faceted feature image set includes a front feature image, a side feature image, and a front feature image after packaging.

[0012] Preferably, the analysis process of the image analysis unit is as follows:

[0013] A pre-set surface defect recognition model for detecting the surface of finished toothbrush products and packaging is obtained. The pre-processed multi-faceted feature image set is sequentially input into the pre-set surface defect recognition model, and the output results of the pre-set surface defect recognition model are obtained. The output results include defect type, defect occurrence time, and whether it is qualified or unqualified.

[0014] If the output result is unqualified, mark the time when the defect occurs as A based on the output result, and mark A sequentially on the time axis corresponding to the processing period of the toothbrush production line.

[0015] Preferably, S1: Set the first A on the time axis as the starting point, obtain the time interval between the first A and the second A, if the time interval between two adjacent A is less than a preset threshold, set the second A as a key point, obtain the time interval between the key point and the next adjacent A, if the time interval between the key point and the next adjacent A is less than a preset threshold, then set the next adjacent A as a key point, if the time interval between two adjacent A is greater than or equal to the preset threshold, then set the next adjacent A as the ending point;

[0016] S2: If the time interval between two adjacent A's is greater than or equal to the preset threshold, then the second A is set as the new starting point, and then S1 is processed again.

[0017] S3: Obtain the duration between the start point and the end point, and set the duration between the start point and the end point as the frequently defective period. Obtain the interval duration between two adjacent frequently defective periods, and merge the two adjacent frequently defective periods with an interval duration less than the preset interval duration threshold to obtain the frequently defective interval.

[0018] Preferably, the defect types of each A in the defect frequency range are obtained, and the defect types of each A are judged. If the defect types of each A are the same, the defect frequency range is set as a single type range. If the defect types of each A are not the same, the defect frequency range is set as a multi-type range.

[0019] Preferably, the analysis process of the defect factor acquisition unit is as follows:

[0020] Based on a single type range, the toothbrush part and the packaging part corresponding to the finished toothbrush are obtained. At the same time, the processing steps of the corresponding toothbrush part and the packaging part are obtained, and the equipment of the processing steps of the corresponding toothbrush part and the equipment of the processing steps of the packaging part are set as associated equipment.

[0021] Preliminary detection discrimination analysis was performed on the detection items of related equipment in the frequently defective range to obtain the detection output results of the detection items of related equipment. The detection output results include those that meet the conditions and those that do not meet the conditions.

[0022] The detection output results of the detection items are analyzed. If all the detection output results of the detection items meet the conditions, an external normal signal is generated. If the detection output results of the detection items do not meet the conditions, the detection items whose detection output results do not meet the conditions are set as suspicious interference items.

[0023] Preferably, the processing basic information of the associated equipment in the frequently defective range is obtained, the processing basic information is input into the pre-set equipment health scoring model, the equipment health score output by the pre-set equipment health scoring model is obtained, and the equipment health score is discriminated to obtain the interfering equipment or the normal equipment.

[0024] Preferably, the analysis process of the production line planning unit is as follows:

[0025] The toothbrush production line for the current batch is divided into a processing area and a packaging area. The number of defective finished toothbrushes in the processing area and the number of defective packages in the packaging area are obtained. The number of defective finished toothbrushes in the processing area and the number of defective packages in the packaging area are set as the number of processing defects and the number of packaging defects, respectively.

[0026] The number of processing defects and the number of packaging defects are processed separately to obtain front-end defect signals, front-end normal signals, back-end defect signals and back-end normal signals. The front-end defect signals, front-end normal signals, back-end defect signals and back-end normal signals are interactively analyzed to obtain production line management signals or single management signals or processing normal signals.

[0027] Preferably, when a production line management signal is generated, the sum of the number of processing defects and the number of packaging defects is obtained, and the sum of the number of processing defects and the number of packaging defects is set as the production line defect value. The production line defect value is obtained as the production line level value set in the corresponding preset production line defect value range, and the dynamic management cycle of the toothbrush production line is obtained based on the production line level value.

[0028] The beneficial effects of this invention are as follows:

[0029] (1) This invention is based on model perspective analysis to quickly identify whether the surface images of the collected toothbrush finished products and packaging are defective. Based on the premise that there are defects, it further summarizes the concentrated distribution period of defects in the toothbrush finished products and packaging, so as to quickly identify the defective interval of the toothbrush production line and display the type distribution of defects in the defective interval, which is convenient for subsequent management.

[0030] (2) In this invention, the factors causing defects in the frequently occurring defect range are analyzed from both internal and external factors. That is, it is determined whether the occurrence of a single type range is caused by interference from internal or external factors of the associated equipment. Then, the interference factors of the associated equipment are identified through analysis so as to carry out targeted management of the interference factors and greatly reduce the frequency of occurrence of a single type.

[0031] (3) The present invention divides the toothbrush production line into a processing area and a packaging area, and with the accompanying zone control analysis, it determines whether the defects of the toothbrush production line are mainly in the processing area or the packaging area, which facilitates the improvement of management targeting and management effect. The overall management of the toothbrush production line is managed by collecting data in a hierarchical manner and dynamically adjusting the operation and maintenance cycle, so as to reduce the overall defect rate of the toothbrush production line. Attached Figure Description

[0032] The invention will now be further described with reference to the accompanying drawings;

[0033] Figure 1 This is a flowchart of the system of the present invention;

[0034] Figure 2 This is a partial reference diagram of the present invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments;

[0037] Example 1: Please refer to Figures 1 to 2 As shown, the present invention is a toothbrush finished product and packaging defect detection system based on machine vision, including a toothbrush defect detection center, an image processing unit, an image analysis unit, a defect factor acquisition unit, a production line planning unit, and a visual feedback unit. The toothbrush defect detection center and the image processing unit have a one-way communication connection, the image processing unit and the image analysis unit have a one-way communication connection, the toothbrush defect detection center and the defect factor acquisition unit and the production line planning unit have a two-way communication connection, and the defect factor acquisition unit and the production line planning unit have a one-way communication connection with the visual feedback unit.

[0038] The toothbrush defect detection center is used to retrieve multi-faceted feature images of finished toothbrushes and their packaging collected by multiple sets of industrial cameras and light source equipment on the toothbrush production line.

[0039] In embodiments of the present invention, multiple high-resolution industrial cameras are distributed on the toothbrush production line. After the toothbrush is assembled, industrial cameras are set directly above and to the side of the production line to capture front and side images of the finished toothbrush.

[0040] During the packaging process, industrial cameras are installed above and at both ends of the packaging conveyor belt to capture images of the front, sides, and sealing of the packaging. The cameras are equipped with high-precision lenses to clearly capture the detailed features of the toothbrush and packaging.

[0041] The image processing unit is used to preprocess the multi-faceted feature image set of the collected toothbrush product and packaging. The preprocessing includes operations such as noise reduction, grayscale conversion, and contrast enhancement. The multi-faceted feature image set includes front feature image, side feature image, and front feature image after packaging.

[0042] The filtering algorithm is used to remove noise interference in the image, the color image is converted to a grayscale image through grayscale conversion, and the image contrast is enhanced by methods such as histogram equalization, thereby improving the clarity of the flaw features in the image.

[0043] The image analysis unit is used to perform defect identification and distribution segmentation analysis on the preprocessed multi-faceted feature image set to determine whether there are defects in the finished toothbrush and its packaging, and the time periods in which defects are concentrated. The specific defect identification and distribution segmentation analysis process is as follows:

[0044] A pre-set surface defect recognition model for inspecting the surface of finished toothbrushes and their packaging is obtained. The pre-processed multi-faceted feature image set is sequentially input into the pre-set surface defect recognition model, and the output results of the pre-set surface defect recognition model are obtained. The output results include defect type, defect occurrence time, pass or fail, etc.; defect types include missing or bent toothbrush bristles, cracks or scratches on the toothbrush handle, etc.

[0045] Among them, the pre-set surface defect recognition model has been trained on a large number of toothbrush images containing various types of defects, and can quickly and accurately identify defects.

[0046] If the output result is unqualified, mark the time when the defect occurs as A based on the output result, and mark A sequentially on the time axis corresponding to the processing period of the toothbrush production line;

[0047] S1: Set the first A on the timeline as the starting point, obtain the time interval between the first A and the second A. If the time interval between two adjacent A is less than a preset threshold, set the second A as a key point, obtain the time interval between the key point and the next adjacent A. If the time interval between the key point and the next adjacent A is less than a preset threshold, set the next adjacent A as a key point. If the time interval between two adjacent A is greater than or equal to the preset threshold, set the next adjacent A as the end point, and so on.

[0048] S2: If the time interval between two adjacent A's is greater than or equal to the preset threshold, then the second A is set as the new starting point, and then S1 is processed again.

[0049] S3: Obtain the duration between the start point and the end point, and set the duration between the start point and the end point as the period with frequent defects. Obtain the interval duration between two adjacent periods with frequent defects, and merge the two adjacent periods with frequent defects whose interval duration is less than the preset interval duration threshold to obtain the period with frequent defects.

[0050] Obtain the defect type of each A in the defect frequency interval, and perform discrimination processing on the defect type of each A. If the defect type of each A is the same, then set the defect frequency interval as a single type interval. If the defect type of each A is not the same, then set the defect frequency interval as a multi-type interval.

[0051] The visual feedback unit is used to respond to a single type range or multiple type ranges in order to display the preset warning text corresponding to the single type range or multiple type ranges. That is, different visual feedback operations are performed based on the feedback situation, so as to intuitively understand the time distribution and defect distribution of the finished toothbrush.

[0052] In embodiments of the present invention, processing defects in finished toothbrush products and packaging are initially identified, and the finished toothbrush products and packaging are managed based on the identification results. Specifically, the distribution of the time when defects appear in finished toothbrush products and packaging is analyzed to identify the time periods in which defects appear, so as to provide data support for subsequent analysis. At the same time, the defect types are further analyzed based on the defect concentration to determine the main defect types during the defect concentration periods, so as to further analyze the causes of defect types.

[0053] Example 2: The defect factor acquisition unit is used to perform multi-angle analysis of defect occurrence factors in related equipment, and to determine whether the occurrence of a single type of defect is caused by internal or external factors of the related equipment. That is, by analyzing and identifying the interfering factors of the related equipment, targeted management can be carried out to significantly reduce the frequency of single types of defects. The specific multi-angle analysis process of defect occurrence factors is as follows:

[0054] Based on a single type range, the toothbrush part and the packaging part corresponding to the finished toothbrush are obtained. At the same time, the processing steps of the corresponding toothbrush part and the packaging part are obtained, and the equipment of the processing steps of the corresponding toothbrush part and the equipment of the processing steps of the packaging part are set as associated equipment.

[0055] Set up the testing items for the associated equipment, including environmental testing, raw material testing, etc.

[0056] Preliminary detection discrimination analysis was performed on the detection items of related equipment in the frequently defective range to obtain the detection output results of the detection items of related equipment. The detection output results include those that meet the conditions and those that do not meet the conditions.

[0057] The detection output results of the detection items are analyzed. If the detection output results of the detection items meet the conditions, an external normal signal is generated. If the detection output results of the detection items do not meet the conditions, the detection items whose detection output results do not meet the conditions are set as suspicious interference items.

[0058] In the embodiments of the present invention, the detection items can be understood as external factor detection items of the associated equipment, and the possible external influences are locked one by one in order to determine the possible reasons for the frequent occurrence of the defect type corresponding to a single type range. Among them, environmental detection is to detect information such as environmental temperature and environmental humidity, and raw material detection is to detect whether the raw materials have changed.

[0059] The processing basic information of the associated equipment in the frequently defective range is obtained. The processing basic information includes electrical parameters and characteristic parameters. The electrical parameters include the average operating current and the average operating voltage, and the characteristic parameters include the vibration amplitude and the operating temperature.

[0060] The processing basic information is input into the pre-set equipment health scoring model to obtain the equipment health score output by the pre-set equipment health scoring model. The equipment health score is then judged. If the equipment health score is lower than the preset equipment health score threshold, the corresponding associated equipment is determined to be an interfering equipment. If the equipment health score is not lower than the preset equipment health score threshold, the corresponding associated equipment is determined to be a normal equipment.

[0061] The visual feedback unit is used to respond to suspicious interference items, interfering devices, or regular equipment. Based on the feedback of suspicious interference items, interfering devices, or regular equipment, the corresponding preset warning text is displayed. That is, based on the feedback information, the possible factors for the occurrence of a single defect type in the frequent defect range are displayed, namely external factors (environment, raw materials) or internal factors (equipment itself), so as to carry out targeted and rational management based on the feedback situation, which helps to reduce the defect occurrence rate.

[0062] In embodiments of the present invention, in-depth analysis is conducted on defects of the same type that frequently occur within a certain period of time. This helps to identify the reasons for the frequent occurrence of defects of the same type, and then to carry out targeted management, which helps to reduce the frequency of defects.

[0063] Example 3: The production line planning unit is used to perform zoned control and cycle planning analysis of the processing and packaging areas of the toothbrush production line. It determines whether defects in the toothbrush production line primarily occur in the processing or packaging area, and dynamically adjusts the management cycle of the toothbrush production line. The specific zoned control and cycle planning analysis process is as follows:

[0064] The toothbrush production line for the current batch is divided into a processing area and a packaging area. The number of defective finished toothbrushes in the processing area and the number of defective packages in the packaging area are obtained. The number of defective finished toothbrushes in the processing area and the number of defective packages in the packaging area are set as the number of processing defects and the number of packaging defects, respectively.

[0065] The number of processing defects and the number of packaging defects are then separately judged and processed:

[0066] If the number of processing defects exceeds the preset threshold for the number of processing defects, a front-end defect signal is generated.

[0067] If the number of processing defects is less than or equal to the preset threshold for the number of processing defects, a front-end normal signal is generated;

[0068] If the number of packaging defects exceeds the preset packaging defect threshold, a backend defect signal is generated.

[0069] If the number of packaging defects is less than or equal to the preset packaging defect threshold, a backend normal signal is generated;

[0070] Interactive analysis of front-end defect signals, front-end normal signals, back-end defect signals, and back-end normal signals:

[0071] If front-end defect signals and back-end defect signals are generated, then production line management signals are obtained;

[0072] If a front-end defect signal and a back-end normal signal are generated, or a front-end normal signal and a back-end defect signal are generated, a single management signal is obtained. The visual feedback unit is used to respond to the single management signal and immediately execute the front-end defect signal or back-end defect signal output by the single management signal feedback to make targeted management adjustments to the processing area or packaging area of ​​the toothbrush production line in order to reduce the defect rate.

[0073] If front-end routine signals and back-end routine signals are generated, a processing routine signal is obtained. The visual feedback unit is used to respond to the processing routine signal and immediately display the preset warning text corresponding to the processing routine signal so as to intuitively understand the routine processing performance of the toothbrush production line.

[0074] When a production line management signal is generated, the sum of the number of processing defects and the number of packaging defects is obtained, and this sum is set as the production line defect value. The production line defect value is determined to be within the corresponding preset production line defect value range, and the dynamic management cycle of the toothbrush production line is obtained based on the production line level value. The visual feedback unit is used to respond to the dynamic management cycle and displays the management plan corresponding to the dynamic management cycle. The management cycle of the toothbrush production line is then shortened according to the dynamic management cycle to improve the production qualification rate and stability of the toothbrush production line.

[0075] In the embodiments of the present invention, the preset production line defect value range is set in ascending order, the production line grade value is set in ascending order, and the larger the value is, the higher the management requirements of the toothbrush production line and the shorter the cycle management interval.

[0076] In summary, this invention is based on model-based analysis to quickly identify whether the surface images of the collected finished toothbrush products and packaging are defective. Furthermore, based on the premise that defects exist, it summarizes the concentrated distribution periods of defects in the finished toothbrush products and packaging in order to quickly identify the defect-prone intervals of the toothbrush production line.

[0077] At the same time, it displays the distribution of defect types in the frequently defective range, and analyzes the factors that cause defects in the frequently defective range from both internal and external factors. That is, it determines whether the occurrence of a single type range is caused by interference from internal or external factors of related equipment. Then, by analyzing and identifying the interference factors of related equipment, targeted management can be carried out to greatly reduce the frequency of single types.

[0078] The toothbrush production line is divided into processing and packaging areas, and with the accompanying zone control analysis, it is determined whether the defects in the toothbrush production line mainly occur in the processing or packaging areas, which facilitates the improvement of management targeting and effectiveness. The overall management of the toothbrush production line is managed by collecting data in a hierarchical manner and dynamically adjusting the operation and maintenance cycle, so as to reduce the overall defect rate of the toothbrush production line.

[0079] The threshold is set for comparative analysis of results to determine whether they are good or bad. The value of the threshold is determined by a combination of large-scale model analysis of sample data and human experience. It can also be adjusted appropriately based on seasonal or common-sense influencing factors.

[0080] The size of the coefficient is a specific value obtained by quantifying each parameter to facilitate subsequent comparison. The size of the coefficient depends on the amount of sample data and the corresponding operating coefficient initially set by those skilled in the art for each set of sample data; as long as it does not affect the proportional relationship between the parameter and the quantified value.

[0081] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A machine vision-based toothbrush finished product and packaging defect detection system, characterized in that, It includes a toothbrush defect detection center, an image processing unit, an image analysis unit, a defect factor acquisition unit, a production line planning unit, and a visual feedback unit; The toothbrush defect detection center is used to retrieve multi-faceted feature images of finished toothbrushes and their packaging collected by multiple sets of industrial cameras and light source equipment on the toothbrush production line. Image processing unit: used to preprocess the multi-faceted feature image set of the collected toothbrush products and packaging; Image analysis unit: used to perform defect identification and distribution analysis on the preprocessed multi-faceted feature image set, to determine whether there are defects in the finished toothbrush and packaging, and to perform discrimination processing on the defect type of each A obtained to obtain a single type interval or multiple type intervals; Defect Factor Acquisition Unit: Used to perform multi-angle analysis of the factors causing defects in related equipment, determine whether the occurrence of a single type of interval is caused by internal or external factors of the related equipment, and obtain external normal signals or suspicious interference items or interfering equipment or conventional equipment. Production line planning unit: used to perform zoned control and cycle planning analysis of the processing and packaging areas of the toothbrush production line, determine whether the defects of the toothbrush production line occur in the processing area or the packaging area, and obtain production line management signals, single management signals or processing routine signals; The multi-faceted feature image set includes front feature images, side feature images, and front feature images after packaging; The analysis process of the image analysis unit is as follows: A pre-set surface defect recognition model for detecting the surface of finished toothbrush products and packaging is obtained. The pre-processed multi-faceted feature image set is sequentially input into the pre-set surface defect recognition model, and the output results of the pre-set surface defect recognition model are obtained. The output results include defect type, defect occurrence time, and whether it is qualified or unqualified. If the output result is unqualified, mark the time when the defect occurs as A based on the output result, and mark A sequentially on the time axis corresponding to the processing period of the toothbrush production line.

2. The machine vision-based toothbrush finished product and packaging defect detection system according to claim 1, characterized in that, S1: Set the first A on the timeline as the starting point, obtain the time interval between the first A and the second A. If the time interval between two adjacent A is less than a preset threshold, set the second A as a key point, obtain the time interval between the key point and the next adjacent A. If the time interval between the key point and the next adjacent A is less than a preset threshold, set the next adjacent A as a key point. If the time interval between two adjacent A is greater than or equal to the preset threshold, set the next adjacent A as the ending point. S2: If the time interval between two adjacent A's is greater than or equal to the preset threshold, then the second A is set as the new starting point, and then S1 is processed again. S3: Obtain the duration between the start point and the end point, and set the duration between the start point and the end point as the frequently defective period. Obtain the interval duration between two adjacent frequently defective periods, and merge the two adjacent frequently defective periods with an interval duration less than the preset interval duration threshold to obtain the frequently defective interval.

3. The machine vision-based toothbrush finished product and packaging defect detection system according to claim 2, characterized in that, Obtain the defect type of each A in the defect frequency interval, and perform discrimination processing on the defect type of each A. If the defect type of each A is the same, then set the defect frequency interval as a single type interval. If the defect type of each A is not the same, then set the defect frequency interval as a multi-type interval.

4. The machine vision-based toothbrush finished product and packaging defect detection system according to claim 1, characterized in that, The analysis process of the defect factor acquisition unit is as follows: Based on a single type range, the toothbrush part and the packaging part corresponding to the finished toothbrush are obtained. At the same time, the processing steps of the corresponding toothbrush part and the packaging part are obtained, and the equipment of the processing steps of the corresponding toothbrush part and the equipment of the processing steps of the packaging part are set as associated equipment. Preliminary detection discrimination analysis was performed on the detection items of related equipment in the frequently defective range to obtain the detection output results of the detection items of related equipment. The detection output results include those that meet the conditions and those that do not meet the conditions. The detection output results of the detection items are analyzed. If all the detection output results of the detection items meet the conditions, an external normal signal is generated. If the detection output results of the detection items do not meet the conditions, the detection items whose detection output results do not meet the conditions are set as suspicious interference items.

5. The machine vision-based toothbrush finished product and packaging defect detection system according to claim 4, characterized in that, The processing basic information of the associated equipment in the frequently defective range is obtained, and the processing basic information is input into the pre-set equipment health scoring model to obtain the equipment health score output by the pre-set equipment health scoring model. The equipment health score is then processed to identify the interfering equipment or the normal equipment.

6. The machine vision-based toothbrush finished product and packaging defect detection system according to claim 1, characterized in that, The analysis process for the production line planning unit is as follows: The toothbrush production line for the current batch is divided into a processing area and a packaging area. The number of defective finished toothbrushes in the processing area and the number of defective packages in the packaging area are obtained. The number of defective finished toothbrushes in the processing area and the number of defective packages in the packaging area are set as the number of processing defects and the number of packaging defects, respectively. The number of processing defects and the number of packaging defects are processed separately to obtain front-end defect signals, front-end normal signals, back-end defect signals and back-end normal signals. The front-end defect signals, front-end normal signals, back-end defect signals and back-end normal signals are interactively analyzed to obtain production line management signals or single management signals or processing normal signals.

7. The machine vision-based toothbrush finished product and packaging defect detection system according to claim 6, characterized in that, When a production line management signal is generated, the sum of the number of processing defects and the number of packaging defects is obtained, and the sum of the number of processing defects and the number of packaging defects is set as the production line defect value. The production line defect value is obtained as the production line level value set in the corresponding preset production line defect value range. The dynamic management cycle of the toothbrush production line is obtained based on the production line level value.

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