Filling and sealing production line appearance real-time detection system and method

Abstract

The invention relates to the technical field of medicine and food filling and sealing production, and discloses a filling and sealing production line appearance real-time detection system and method, and the system comprises a filling and sealing machine module, an optical detection module, an abnormality analysis module, a control module, a storage module, an alarm module, a data transmission module and a display module. The filling and sealing machine module comprises a filling head, a conveying belt and a sealing device, the filling and sealing machine module, an optical detection module, an abnormity analysis module, a control module, a storage module, an alarm module, a data transmission module and a display module are integrated, and through the optical scanning and intelligent analysis technology, appearance defects are found in real time in the filling and sealing process; the appearance of the encapsulation production line is detected in real time, the problems of detection hysteresis and resource waste in the prior art are effectively solved, equipment faults or abnormal conditions can be detected and processed in time, the stability and the production efficiency of the production line are improved, and the product quality is ensured.

Description

Technical Field

[0001] The present invention relates to the technical field of pharmaceutical and food filling and sealing production, and particularly relates to a real-time appearance detection system and method for a filling and sealing production line. Background Art

[0002] In the field of pharmaceutical and food filling and sealing production, a filling and sealing machine is used to fill a liquid into a container (such as a glass bottle or a plastic bottle) and complete the sealing. In a traditional production line, an independent lamp inspection process is usually set up after filling and sealing, and the appearance of the product is detected by manual or semi-automatic equipment, such as detecting the tightness of the bottle mouth, the integrity of the label, the turbidity of the liquid, etc.

[0003] However, the prior art has the problem of detection lag. The appearance inspection is located at the end of production. When a batch of defects is found, the whole batch of products has been produced, resulting in high rework costs. There is also a problem of resource waste. Defective products may have occupied a large amount of raw materials and energy, and the downtime for adjustment is long, affecting production efficiency. At the same time, it relies on manual intervention. The traditional lamp inspection process relies on manual visual inspection or simple sensors, with a high missed detection rate and no real-time feedback to the filling and sealing machine control system.

[0004] Therefore, we propose a real-time appearance detection system and method for a filling and sealing production line. Summary of the Invention

[0005] The present invention mainly solves the technical problems existing in the above prior art, and provides a real-time appearance detection system and method for a filling and sealing production line.

[0006] To achieve the above object, the present invention adopts the following technical solutions. A real-time appearance detection system for a filling and sealing production line includes a filling and sealing machine module, an optical detection module, an anomaly analysis module, a control module, a storage module, an alarm module, a data transmission module, and a display module. The filling and sealing machine module is used to perform the functions of liquid filling and preliminary sealing. The filling and sealing machine module includes a filling head, a conveyor belt, and a sealing device.

[0007] Preferably, the optical detection module is installed at the rear end of the filling and sealing machine module. The optical detection module is used to perform an omnidirectional optical scan of the product appearance to detect whether there are appearance defects.

[0008] Preferably, the optical detection module includes multi-angle light sources, a high-resolution camera, and an image processor.

[0009] Preferably, the anomaly analysis module is used to receive the detection data from the lamp inspection device and analyze whether there are a large number of abnormal products. The anomaly analysis module, based on a machine learning algorithm, extracts features and classifies the image data to identify defects such as scratches, bubbles, and label misalignment.

[0010] Preferably, the anomaly analysis module includes a feature extraction unit, a feature classification unit, and a feature determination unit.

[0011] Preferably, the control module is used to control the operation of the production line according to the output of the anomaly analysis module, such as pausing, restarting, or adjusting the speed, to ensure that defective products are not further processed.

[0012] Preferably, the control module includes a control unit and an execution unit.

[0013] Preferably, the storage module is used to store the detection data, analysis results, and operation status information of the production line, facilitating subsequent data traceability and analysis.

[0014] Preferably, the storage module includes a data storage unit and a data backup unit.

[0015] Preferably, the alarm module is used to send an alarm signal in a timely manner when an abnormal product is detected, notifying the operator to intervene.

[0016] Preferably, the alarm module includes an alarm signal generator and an indicator light.

[0017] Preferably, the data transmission module is used to transmit the detection data, analysis results, and production line status information to the remote monitoring center in real time, realizing remote monitoring and management, and improving production efficiency and response speed.

[0018] Preferably, the data transmission module includes a data transmission unit and a communication protocol processing unit.

[0019] Preferably, the display module is used to display the detection data, analysis results, and operation status of the production line in real time, facilitating the operator to intuitively understand the operation of the production line.

[0020] Preferably, the display module includes a high-definition display screen and a user interface.

[0021] A method for real-time detection of the appearance of an encapsulation production line, including the above-mentioned real-time detection system for the appearance of an encapsulation production line, specifically includes the following steps:

[0022] The first step: filling and sealing: The liquid in the container of the medicine or food is filled through the filling head in the encapsulation machine module, and then the filled container is sent to the sealing device through the conveyor belt for preliminary sealing;

[0023] The second step: optical scanning: The sealed product is scanned optically in all directions by using the multi-angle light source and high-resolution camera in the optical detection module to ensure that all surfaces of the product can be clearly imaged, and the image processor processes the collected images for subsequent analysis;

[0024] Step 3: Abnormality Detection and Analysis: The abnormality analysis module receives the image data from the optical detection module, extracts features and classifies the image data through machine learning algorithms, identifies appearance defects such as scratches, bubbles, and label misalignment, and determines whether there are a large number of abnormal products. If there are abnormal products, an abnormal signal is output to the control module;

[0025] Step 4: Control Response: After receiving the abnormal signal, the control module immediately controls the production line according to the preset strategy, such as pausing the operation of the production line to prevent defective products from entering the next process, or adjusting the speed of the production line so that the operator has enough time to intervene;

[0026] Step 5: Data Storage and Alarm: The storage module stores the data of this detection, the analysis results, and the operation status information of the production line for subsequent data traceability and analysis. At the same time, if abnormal products are detected, the alarm module will send out an alarm signal in time, and notify the operator to intervene through the alarm signal generator and indicator lights to ensure the stable operation of the production line and product quality.

[0027] The present invention provides a real-time appearance detection system and method for a potting production line. It has the following beneficial effects:

[0028] 1. The real-time appearance detection system and method for a potting production line integrates a potting machine module, an optical detection module, an abnormality analysis module, a control module, a storage module, an alarm module, a data transmission module, and a display module. Through optical scanning and intelligent analysis technology, appearance defects can be immediately discovered during the potting process, and an alarm or shutdown mechanism is triggered to reduce the generation of unqualified products. It realizes the real-time detection of the appearance of the potting production line, effectively solves the problems of detection lag and resource waste in the prior art, can detect and handle equipment failures or abnormal situations in time, improves the stability and production efficiency of the production line, and ensures product quality.

[0029] 2. The real-time appearance detection system and method for a potting production line sets an optical detection module and an abnormality analysis module. By moving the optical detection module forward to the rear end of the potting machine, appearance abnormalities can be discovered in real time during the production process, avoiding the batch generation of unqualified products caused by detection lag. The high-resolution camera and image processor of the optical detection module ensure the high-precision detection of the product appearance. The abnormality analysis module is based on machine learning algorithms and can accurately identify various appearance defects, improving the accuracy and reliability of detection.

[0030] 3. The real-time appearance detection system and method for a potting production line sets a control module. According to the output of the abnormality analysis module, the control module can adjust the operation of the production line in real time, effectively avoiding the further processing of defective products and reducing the rework cost.

[0031] 4. The real-time appearance detection system and method for an encapsulation production line, by setting a storage module and an alarm module, the combined use of the storage module and the alarm module not only facilitates subsequent data traceability and analysis, but also can issue an alarm in time when detecting abnormal products, notify the operator to intervene, and ensure the stable operation of the production line and product quality.

[0032] 5. The real-time appearance detection system and method for an encapsulation production line, by setting a data transmission module and a display module, the addition of the data transmission module and the display module realizes remote monitoring and management, and improves production efficiency and response speed. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 is the system architecture diagram of the present invention;

[0034] Figure 2 is the method flow chart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0035] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the drawings in the following description are only exemplary, and for those of ordinary skill in the art, without creative efforts, other implementation drawings can also be obtained according to the provided drawings.

[0036] The structures, ratios, sizes, etc. shown in this specification are only used to cooperate with the content disclosed in the specification for those who are familiar with this technology to understand and read, and are not used to limit the limited conditions under which the present invention can be implemented. Therefore, they do not have technical substance. Any modification of the structure, change of the proportional relationship or adjustment of the size, without affecting the effects that the present invention can produce and the purposes that can be achieved, should still fall within the scope that can be covered by the technical content disclosed in the present invention.

[0037] It should be noted that: similar reference numerals and letters represent similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings.

[0038] In the description of the embodiments of the present invention, it should be noted that the orientation or positional relationship indicated by terms such as "center", "upper", "lower", "inner", "outer", "side", etc. is based on the orientation or positional relationship shown in the drawings, or the orientation or positional relationship in which the product of the invention is customarily placed during use. It is only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be construed as a limitation to the present invention. In addition, terms such as "first" and "second" are only used for descriptive distinction and cannot be understood as indicating or implying relative importance.

[0039] In the description of the embodiments of the present invention, it should also be noted that unless otherwise clearly specified and defined, the terms "set", "install", "connect", "couple" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those of ordinary skill in the art, the specific meanings of the above terms in the embodiments of the present invention can be understood according to specific situations.

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0041] Embodiment 1: A real-time appearance detection system for an encapsulation production line, as Figure 1 shown, includes an encapsulation machine module, an optical detection module, an anomaly analysis module, a control module, a storage module, an alarm module, a data transmission module, and a display module. The encapsulation machine module is used to perform the functions of liquid filling and preliminary sealing. The encapsulation machine module includes a filling head, a conveyor belt, and a sealing device. The optical detection module is installed at the rear end of the encapsulation machine module. The optical detection module is used to perform an all-round optical scan of the product appearance to detect whether there are appearance defects. The optical detection module includes multi-angle light sources, a high-resolution camera, and an image processor. By integrating the encapsulation machine module, the optical detection module, the anomaly analysis module, the control module, the storage module, the alarm module, the data transmission module, and the display module, through optical scanning and intelligent analysis technology, appearance defects can be immediately detected during the encapsulation process, and the alarm or shutdown mechanism can be triggered to reduce the generation of unqualified products, realizing the real-time detection of the appearance of the encapsulation production line, effectively solving the problems of detection lag and resource waste in the prior art, being able to detect and handle equipment failures or abnormal situations in a timely manner, improving the stability and production efficiency of the production line, and ensuring the quality of the products.

[0042] Example 2: On the basis of Example 1, as Figure 1 shown, the anomaly analysis module is used to receive the detection data from the lamp inspection device and analyze whether there are a large number of defective products. The anomaly analysis module extracts and classifies features from the image data based on machine learning algorithms to identify defects such as scratches, bubbles, and label misalignment. The anomaly analysis module includes a feature extraction unit, a feature classification unit, and a feature determination unit. The control module is used to control the operation of the production line according to the output of the anomaly analysis module, such as pausing, restarting, or adjusting the speed, to ensure that defective products are not further processed. The control module includes a control unit and an execution unit. By setting up the optical detection module and the anomaly analysis module, and moving the optical detection module forward to the rear end of the potting machine, it is possible to detect appearance anomalies in real time during the production process, avoiding the batch production of unqualified products caused by detection lag. The high-resolution camera and image processor of the optical detection module ensure the high-precision detection of the product appearance. The anomaly analysis module, based on machine learning algorithms, can accurately identify various appearance defects, improving the accuracy and reliability of the detection.

[0043] Example 3: On the basis of Example 1 and Example 2, as Figure 1 shown, the storage module is used to store the detection data, analysis results, and the operation status information of the production line, facilitating subsequent data traceability and analysis. The storage module includes a data storage unit and a data backup unit. The alarm module is used to send an alarm signal in a timely manner when defective products are detected, notifying the operator to intervene. The alarm module includes an alarm signal generator and an indicator light. By setting up the control module, the control module can adjust the operation of the production line in real time according to the output of the anomaly analysis module, effectively avoiding the further processing of defective products and reducing the rework cost.

[0044] Example 4: On the basis of Example 1, Example 2, and Example 3, as Figure 1 shown, the data transmission module is used to transmit the detection data, analysis results, and production line status information to the remote monitoring center in real time, realizing remote monitoring and management, and improving production efficiency and response speed. The data transmission module includes a data transmission unit and a communication protocol processing unit. The display module is used to display the detection data, analysis results, and the operation status of the production line in real time, facilitating the operator to intuitively understand the operation of the production line. The display module includes a high-definition display screen and a user interface. By setting up the storage module and the alarm module, the combined use of the storage module and the alarm module not only facilitates subsequent data traceability and analysis, but also can send an alarm in a timely manner when defective products are detected, notifying the operator to intervene, ensuring the stable operation of the production line and product quality.

[0045] Example 5: On the basis of Example 1, Example 2, Example 3, and Example 4, as Figure 2As shown in the figure, a real-time detection method for the appearance of an encapsulation production line, including the above-mentioned real-time detection system for the appearance of the encapsulation production line, specifically includes the following steps: The first step: filling and sealing: The liquid in the container of medicine or food is filled through the filling head in the encapsulation machine module, and then the filled container is sent to the sealing device through the conveyor belt for preliminary sealing; The second step: optical scanning: The sealed product is scanned optically in all directions by using the multi-angle light source and high-resolution camera in the optical detection module to ensure that all surfaces of the product can be clearly imaged, and the image processor processes the collected images for subsequent analysis; The third step: abnormal detection and analysis: The abnormal analysis module receives the image data from the optical detection module, extracts features and classifies the image data through machine learning algorithms, identifies appearance defects such as scratches, bubbles, and label misalignment, and judges whether there are a large number of abnormal products. If there are abnormal products, an abnormal signal is output to the control module; The fourth step: control response: After receiving the abnormal signal, the control module immediately controls the production line according to the preset strategy, such as pausing the operation of the production line to prevent defective products from continuing to enter the next process, or adjusting the speed of the production line so that the operator has enough time to intervene; The fifth step: data storage and alarm: The storage module stores the data of this detection, the analysis results, and the operation status information of the production line for subsequent data traceability and analysis. At the same time, if abnormal products are detected, the alarm module will send out an alarm signal in time, and the operator is notified to intervene through the alarm signal generator and indicator light to ensure the stable operation of the production line and product quality. By setting up the data transmission module and the display module, the addition of the data transmission module and the display module realizes remote monitoring and management, improving production efficiency and response speed.

[0046] Working principle of the present invention: First, by using the high-resolution camera and multi-angle light source of the optical detection module, it is possible to achieve a full-range and precise scan of the product appearance, ensuring that any subtle defects can be captured. Secondly, based on advanced machine learning algorithms, the anomaly analysis module conducts intelligent analysis on the collected image data, and can accurately identify various appearance defects such as scratches, bubbles, and label misalignment, greatly improving the accuracy and efficiency of detection. Furthermore, according to the output results of the anomaly analysis module, the control module can quickly respond, such as pausing the operation of the production line or adjusting the speed, thereby effectively avoiding the further processing of defective products and reducing resource waste. At the same time, the collaborative work of the storage module and the alarm module enables the detection data, analysis results, and operation status information of the production line to be recorded and stored in real time. Once an abnormal product is detected, the alarm module will immediately send an alarm signal to notify the operator to intervene, ensuring the stable operation of the production line and product quality. In addition, the introduction of the data transmission module and the display module realizes remote monitoring and management, enabling the operator to grasp the operation status of the production line at any time, further improving the production efficiency and response speed. In summary, the real-time appearance detection system and method for an encapsulation production line have significant technical advantages and application values.

[0047] The above has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. What is described in the above embodiments and the specification only illustrates the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. An appearance real-time detection system for an encapsulation production line, characterized in that, It includes a potting machine module, an optical detection module, an anomaly analysis module, a control module, a storage module, an alarm module, a data transmission module, and a display module. The potting machine module includes a filling head, a conveyor belt, and a sealing device.

2. The appearance real-time detection system of the potting production line according to claim 1, wherein: The optical detection module is installed at the rear end of the potting machine module. The optical detection module includes a multi-angle light source, a high-resolution camera, and an image processor.

3. The real-time appearance detection system for the potting production line according to claim 1, wherein: The anomaly analysis module includes a feature extraction unit, a feature classification unit, and a feature determination unit.

4. The real-time appearance detection system for the potting production line according to claim 1, characterized in that: The control module includes a control unit and an execution unit.

5. The real-time appearance detection system for the potting production line according to claim 1, characterized in that: The storage module includes a data storage unit and a data backup unit.

6. The real-time appearance detection system for the potting production line according to claim 1, wherein: The alarm module includes an alarm signal generator and an indicator light.

7. The appearance real-time detection system for the potting production line according to claim 1, wherein: The data transmission module includes a data transmission unit and a communication protocol processing unit.

8. The real-time appearance detection system for the potting production line according to claim 1, wherein: The display module includes a high-definition display screen and a user interface.

9. A real-time detection method for the appearance of an encapsulation production line, characterized in that, It includes a real-time appearance detection system for the potting production line according to any one of claims 1-8, specifically including the following steps: The first step: filling and sealing: The filling head in the potting machine module is used to perform liquid filling on the containers of drugs or foods, and then the filled containers are sent to the sealing device through the conveyor belt for preliminary sealing. The second step: optical scanning: The multi-angle light source and the high-resolution camera in the optical detection module are used to perform an all-round optical scan on the sealed product to ensure that all surfaces of the product can be clearly imaged, and the image processor processes the collected images for subsequent analysis. The third step: anomaly detection and analysis: The anomaly analysis module receives the image data from the optical detection module, extracts and classifies the features of the image data through machine learning algorithms, identifies appearance defects such as scratches, air bubbles, and label misalignment, and determines whether there are a large number of defective products. If there are defective products, an anomaly signal is output to the control module. The fourth step: control response: After receiving the anomaly signal, the control module immediately controls the production line according to the preset strategy, such as pausing the operation of the production line to prevent defective products from continuing to enter the next process, or adjusting the speed of the production line so that the operator has enough time to intervene. The fifth step: data storage and alarm: The storage module stores the data of this detection, the analysis results, and the operation status information of the production line for subsequent data traceability and analysis. At the same time, if defective products are detected, the alarm module will promptly send an alarm signal to notify the operator to intervene through the alarm signal generator and the indicator light to ensure the stable operation of the production line and the product quality.

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