Aviation product classifying and screening device based on deep learning

Abstract

The invention relates to the technical field of aviation product classification and screening, in particular to an aviation product classification and screening device based on deep learning, which combines a deep learning technology with an internet of things technology to realize intelligent classification, screening and sorting after aviation product processing. Through the classification and screening mode, the labor intensity of operators is reduced, and the accuracy and reliability of classification and screening of the aviation products are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of aviation product classification and screening technology, specifically to an aviation product classification and screening device based on deep learning. This device combines deep learning technology with Internet of Things (IoT) technology to achieve intelligent classification, screening, and sorting of processed aviation products. This classification and screening method not only reduces the labor intensity of operators but also significantly improves the accuracy and reliability of aviation product classification and screening. Background Technology

[0002] With the rapid development of the aviation industry, the variety and quantity of aviation products have exploded, making the need for efficient classification and screening of aviation products increasingly urgent. Traditional classification and screening methods mainly rely on manual operation, which not only brings enormous labor intensity but also struggles to achieve satisfactory levels of accuracy and reliability. Furthermore, considering the special nature of aviation products, safety and efficiency in the classification and screening process have become crucial factors that cannot be ignored. Therefore, developing a device capable of automating and intelligently completing the classification and screening of aviation products has become an urgent priority to improve the accuracy and reliability of classification and screening, reduce the burden on operators, and meet stringent requirements for safety and efficiency.

[0003] Currently, most existing classification and screening devices on the market employ simple mechanical structures or rule-based classification algorithms. However, these methods often perform poorly when faced with complex and ever-changing aviation products, making it difficult to adapt to the rapid growth in the variety and quantity of aviation products. Deep learning, as a cutting-edge machine learning technology, has made breakthroughs in multiple fields such as image recognition and natural language processing, demonstrating powerful data processing and pattern recognition capabilities. Applying deep learning technology to the classification and screening of aviation products can achieve efficient and accurate classification and screening, significantly improving the intelligence level of classification and screening. However, there are still relatively few devices that apply deep learning technology to the classification and screening of aviation products, and most are still in the research and development stage, without yet forming mature product applications. Summary of the Invention

[0004] This invention utilizes deep learning algorithms to deeply analyze and process information from multiple dimensions, such as images and dimensions, of aviation products, thereby achieving efficient and accurate classification and screening. Compared to traditional methods, this invention significantly improves both intelligence and adaptability, better accommodating the rapid growth in the types and quantities of aviation products. Furthermore, this invention prioritizes safety and efficiency, optimizing algorithms and hardware design to ensure no damage is caused to aviation products during the classification and screening process, while significantly improving the speed and accuracy of classification and screening.

[0005] To address the aforementioned problems, this invention provides a deep learning-based aviation product classification and screening device.

[0006] The technical problem solved by this invention is achieved by the following technical solution:

[0007] The device comprises a data acquisition module, a preprocessing module, a deep learning model building module, and a result output module. The data acquisition module collects various information about aerospace products, such as size and shape, as well as any images that may be included. The preprocessing module processes, converts, and standardizes this raw image data to ensure image quality and meet the input requirements of the deep learning model. The deep learning model building module is the core of the device; it uses deep learning algorithms to construct a classification model based on the preprocessed data. Finally, the result output module displays the classification results to the user in an intuitive way, facilitating viewing and use. Through the collaborative work of these modules, the present invention enables efficient and accurate classification and screening of aerospace products, meeting the aerospace industry's demand for intelligent classification and screening devices.

[0008] According to one aspect of this application, a deep learning-based aviation product classification and screening device is provided, which consists of a mechanical structure, a sorting and identification structure, and a control structure.

[0009] The mechanical structure consists of a display screen 1, a sorting area 2, connecting screws 6, a mouse 8, and a keyboard 9.

[0010] The device consists of an XOY axis rotating shaft 112, an XOZ motor 13, an XOZ axis rotating shaft 14, an XOZ arm rotating arm 15, an XOY axis rotating shaft 216, an electric telescopic rod 17, a pin 18, a bearing 19, a clamp 20, a connecting block 21, and a locking nut 22.

[0011] The control structure consists of a telescopic device 3, a bushing 4, a sliding rotating mechanism 5, a slide rail 7, a start button 10, a record button 11, a deep learning camera 23, a signal acquisition module 24, a signal conditioning module 25, a signal processing module 26, a signal control module 27, an IoT gateway 28, and a communication module 29.

[0012] The display screen 1 is a display component used to display the operation interface and classification and filtering results, so that operators can monitor the filtering results and perform emergency operations in real time.

[0013] The sorting area 2 is a storage component for screened parts. Sorting area 2 is responsible for receiving aviation products to be screened and classifying them according to preset classification standards, as shown in the attached diagram. Figure 3As shown, the six sorting areas are divided into one picking area and five storage areas. The device identifies and sorts the parts in the picking area to the storage area. Each sorting area is equipped with a pressure sensor. When a part to be sorted is placed on the sorting area 2, a pressure signal is generated for use by the Internet of Things gateway 28.

[0014] The connecting screw 6 is a connecting component. The connecting screw 6 is used to connect the slide rail 7 to the sorting area 2 to ensure a stable connection and smooth sliding between the components, thereby improving the stability and durability of the entire device.

[0015] The mouse 8 and keyboard 9 are input devices. By modifying the recognition algorithm through the mouse 8 and keyboard 9, the position of one picking area and five storage areas can be controlled to adapt to different environmental conditions. The mouse 8 and keyboard 9 also facilitate operators to input commands and parameters to control the entire sorting and screening process.

[0016] The XOY axis 112, XOZ motor 13, XOZ axis 14, XOZ arm 15, and XOY axis 216 are transmission components. Together they form a multi-dimensional rotation mechanism. The relative movement between them ensures that the electric telescopic rod 17 drives the deep learning camera 23 to photograph and identify aviation products from different angles, thereby improving the accuracy and comprehensiveness of the identification.

[0017] The electric telescopic rod 17, pin 18, bearing 19, gripper 20, connecting block 21, and locking nut 22 are moving action components. The gripper 20 and connecting block 21 are fixed by the pin 18 and bearing 19. The bearing 19 is interference-fitted to both the gripper 20 and the connecting block 21. The interference fit between the pin 18 and the bearing 19 prevents movement and prevents movement in the Z-axis, allowing movement only in the XOY plane. The clever structure enables the gripper 20 to work. During operation, the locking nut 22 fixes the connecting block 21 to the gripper 21 to prevent relative displacement. The extension and retraction of the electric telescopic rod 17 controls the clamping and releasing of the gripper 20, realizing the gripping function of the parts to be sorted.

[0018] The telescopic member 3, along with the bushing 4, the sliding turntable 5, and the slide rail 7, forms a displacement assembly. The telescopic shaft of the telescopic member 3 passes through the bushing 4, which protects the telescopic shaft. The telescopic shaft of the telescopic member 3 is connected to the sliding turntable 5, which transmits the telescopic displacement motion of the telescopic member 3 to the sliding turntable 5. Through the cooperation of these components, the extension and retraction of the sorting arm are realized, ensuring that the sorting and identification structure can accurately reach the designated position using displacement motion, thus realizing the movement of the entire identification and sorting assembly.

[0019] The start button 10 and record button 11 are control components, used to start the classification and filtering program and record the filtering results, respectively, to facilitate subsequent data analysis and processing.

[0020] The deep learning camera 23 is a component for recognizing information. As a core recognition component, it can capture image information of aviation products in real time and perform preprocessing and analysis through the signal acquisition module 24, signal conditioning module 25, and signal processing module 26.

[0021] The signal acquisition module 24, signal conditioning module 25, signal processing module 26, and signal control module 27 are signal acquisition, conditioning, processing, and control components. The signal acquisition module 24 can acquire signals identified by the deep learning camera 23. The acquired signals are conditioned and processed by the signal conditioning module 25 and transformed into signals that can be received by the signal processing module 26. The signal processing module 26 receives and processes the signals, derives control commands, and transmits them to the signal control module 27. The signal control module 27 issues the execution actions of the control commands, thereby realizing the functions of signal acquisition, conditioning, processing, and control.

[0022] The IoT gate 28 and communication module 29 are remote control functional components. When the pressure signal of the sorting area 2 is transmitted to the IoT gate 28, the IoT gate 28 can be activated by the communication module 29 to replace the start button 10. When the pressure is too high, the upper limit threshold of the IoT gate 28 will be triggered, and a warning message will be displayed on the display screen 1, indicating that the sorting area cannot be divided into blocks and no more parts can be placed. The sorting identification structure will also stop placing parts into the storage area blocks to prevent the sorted parts from being sorted.

[0023] The advantages of this application are:

[0024] In practical applications of aviation product sorting and screening, the intelligent sorting and screening device of this invention significantly improves sorting and screening efficiency. By introducing automated processes, it greatly reduces reliance on manual operation, thereby effectively reducing human error and improving the overall accuracy of operations. Simultaneously, the real-time monitoring function of this invention ensures that any deviations occurring during the sorting and screening process can be detected and corrected promptly, further guaranteeing the high-quality standards of sorting and screening work. Furthermore, this intelligent sorting and screening device possesses excellent environmental adaptability, capable of stable operation in various complex and changing production environments, meeting the urgent needs of the aviation manufacturing industry for efficient and high-precision sorting operations.

[0025] Compared with traditional sorting methods, the intelligent sorting and screening device of this invention demonstrates significant advantages in efficiency, accuracy, and cost control. It not only completes sorting and screening tasks quickly and accurately, but also provides strong support for continuous optimization of the production process through real-time monitoring and data analysis. Therefore, this intelligent sorting and screening device has broad application prospects in the aerospace manufacturing industry and is expected to become one of the key technologies driving the intelligent upgrading and transformation of the entire industry.

[0026] In practical applications, this intelligent sorting and screening device has significantly improved the overall safety of aviation product sorting and screening. By reducing manual operations, it lowers the risk of staff exposure to hazardous environments, thereby ensuring personnel safety. Furthermore, the device is equipped with advanced sensors and a data processing system that can monitor various parameters during the sorting and screening process in real time. Once an anomaly is detected, an early warning mechanism can be immediately activated, effectively preventing potential safety hazards.

[0027] In summary, the intelligent sorting device of this invention has demonstrated outstanding implementation results in the field of aerospace parts sorting, achieving significant improvements in efficiency, accuracy, and safety, while also bringing tangible benefits to enterprises in terms of cost control. The successful application of this innovative technology undoubtedly injects new vitality into the intelligent upgrading and transformation of the aerospace manufacturing industry. Attached Figure Description

[0028] Figure 1 This is an isometric view of an aviation product classification and screening device based on deep learning;

[0029] Figure 2 This is the main view of a deep learning-based aviation product classification and screening device;

[0030] Figure 3 This is a top view of a deep learning-based aviation product classification and screening device;

[0031] Figure 4 This is a right view of a deep learning-based aviation product classification and screening device;

[0032] The components include: 1. Display screen; 2. Sorting area; 3. Telescopic device; 4. Shaft sleeve; 5. Sliding rotary machine; 6. Connecting screw; 7. Slide rail; 8. Mouse; 9. Keyboard; 10. Start button; 11. Record button; 12. XOY axis 1; 13. XOZ motor; 14. XOZ axis; 15. XOZ arm; 16. XOY axis 2; 17. Electric telescopic rod; 18. Pin; 19. Bearing; 20. Clamp; 21. Connecting block; 22. Locking nut; 23. Deep learning camera; 24. Signal acquisition module; 25. Signal conditioning module; 26. Signal processing module; 27. Signal control module; 28. IoT gateway; and 29. Communication module. Detailed Implementation

[0033] The present application is described in detail below with reference to the embodiments, but the present application is not limited to these embodiments.

[0034] Example 1

[0035] A deep learning-based aviation product classification and screening device consists of a mechanical structure, a sorting and identification structure, and a control structure.

[0036] The mechanical structure consists of a display screen 1, a sorting area 2, connecting screws 6, a mouse 8, and a keyboard 9.

[0037] The device consists of an XOY axis rotating shaft 112, an XOZ motor 13, an XOZ axis rotating shaft 14, an XOZ arm rotating arm 15, an XOY axis rotating shaft 216, an electric telescopic rod 17, a pin 18, a bearing 19, a clamp 20, a connecting block 21, and a locking nut 22.

[0038] The control structure consists of a telescopic device 3, a bushing 4, a sliding rotating mechanism 5, a slide rail 7, a start button 10, a record button 11, a deep learning camera 23, a signal acquisition module 24, a signal conditioning module 25, a signal processing module 26, a signal control module 27, an IoT gateway 28, and a communication module 29.

[0039] The display screen 1 is a display component used to display the operation interface and classification and filtering results, so that operators can monitor the filtering results and perform emergency operations in real time.

[0040] The sorting area 2 is a storage component for screened parts. Sorting area 2 is responsible for receiving aviation products to be screened and classifying them according to preset classification standards, as shown in the attached diagram. Figure 3 As shown, the six sorting areas are divided into one picking area and five storage areas. The device identifies and sorts the parts in the picking area to the storage area. Each sorting area is equipped with a pressure sensor. When a part to be sorted is placed on the sorting area 2, a pressure signal is generated for use by the Internet of Things gateway 28.

[0041] The connecting screw 6 is a connecting component. The connecting screw 6 is used to connect the slide rail 7 to the sorting area 2 to ensure a stable connection and smooth sliding between the components, thereby improving the stability and durability of the entire device.

[0042] The mouse 8 and keyboard 9 are input devices. By modifying the recognition algorithm through the mouse 8 and keyboard 9, the position of one picking area and five storage areas can be controlled to adapt to different environmental conditions. The mouse 8 and keyboard 9 also facilitate operators to input commands and parameters to control the entire sorting and screening process.

[0043] The XOY axis 112, XOZ motor 13, XOZ axis 14, XOZ arm 15, and XOY axis 216 are transmission components. Together they form a multi-dimensional rotation mechanism. The relative movement between them ensures that the electric telescopic rod 17 drives the deep learning camera 23 to photograph and identify aviation products from different angles, thereby improving the accuracy and comprehensiveness of the identification.

[0044] The electric telescopic rod 17, pin 18, bearing 19, gripper 20, connecting block 21, and locking nut 22 are moving action components. The gripper 20 and connecting block 21 are fixed by the pin 18 and bearing 19. The bearing 19 is interference-fitted to both the gripper 20 and the connecting block 21. The interference fit between the pin 18 and the bearing 19 prevents movement and prevents movement in the Z-axis, allowing movement only in the XOY plane. The clever structure enables the gripper 20 to work. During operation, the locking nut 22 fixes the connecting block 21 to the gripper 21 to prevent relative displacement. The extension and retraction of the electric telescopic rod 17 controls the clamping and releasing of the gripper 20, realizing the gripping function of the parts to be sorted.

[0045] The telescopic member 3, along with the bushing 4, the sliding turntable 5, and the slide rail 7, forms a displacement assembly. The telescopic shaft of the telescopic member 3 passes through the bushing 4, which protects the telescopic shaft. The telescopic shaft of the telescopic member 3 is connected to the sliding turntable 5, which transmits the telescopic displacement motion of the telescopic member 3 to the sliding turntable 5. Through the cooperation of these components, the extension and retraction of the sorting arm are realized, ensuring that the sorting and identification structure can accurately reach the designated position using displacement motion, thus realizing the movement of the entire identification and sorting assembly.

[0046] The start button 10 and record button 11 are control components, used to start the classification and filtering program and record the filtering results, respectively, to facilitate subsequent data analysis and processing.

[0047] The deep learning camera 23 is a component for recognizing information. As a core recognition component, it can capture image information of aviation products in real time and perform preprocessing and analysis through the signal acquisition module 24, signal conditioning module 25, and signal processing module 26.

[0048] The signal acquisition module 24, signal conditioning module 25, signal processing module 26, and signal control module 27 are signal acquisition, conditioning, processing, and control components. The signal acquisition module 24 can acquire signals identified by the deep learning camera 23. The acquired signals are conditioned and processed by the signal conditioning module 25 and transformed into signals that can be received by the signal processing module 26. The signal processing module 26 receives and processes the signals, derives control commands, and transmits them to the signal control module 27. The signal control module 27 issues the execution actions of the control commands, thereby realizing the functions of signal acquisition, conditioning, processing, and control.

[0049] The IoT gate 28 and communication module 29 are remote control functional components. When the pressure signal of the sorting area 2 is transmitted to the IoT gate 28, the IoT gate 28 can be activated by the communication module 29 to replace the start button 10. When the pressure is too high, the upper limit threshold of the IoT gate 28 will be triggered, and a warning message will be displayed on the display screen 1, indicating that the sorting area cannot be divided into blocks and no more parts can be placed. The sorting identification structure will also stop placing parts into the storage area blocks to prevent the sorted parts from being sorted.

[0050] The specific work process is as follows:

[0051] Step 1: Before use, perform a visual inspection of the aviation product classification and screening device and check whether the device labels are complete and the appearance is intact. If they are complete and intact, proceed to the next step.

[0052] Step 2: Press the start button 10 to activate the aviation product classification and screening device. Check if the display screen 1 is normal. If the display is normal, proceed to the next step.

[0053] Step 3: Place the aviation products to be sorted in the sorting area 2. At this time, the sensors in sorting area 2 will generate pressure signals. The more parts to be sorted are placed, the greater the pressure will be. The pressure signal will trigger IoT switch 28. IoT switch 28 controls the aviation product sorting and screening device to start working through communication module 29. When the speed of placing aviation products is greater than the sorting speed, the pressure will exceed the upper limit threshold of IoT switch 28 at a certain point. At this time, IoT switch 28 will be triggered, and a warning message will be displayed on display screen 1, prohibiting the placement of aviation products into the sorting area 2.

[0054] Step 4: Image acquisition is performed using the deep learning camera 23, and information acquisition, conditioning, processing and control are achieved using the signal acquisition module 24, signal conditioning module 25, signal processing module 26 and signal control module 27, so as to give the sorting device action instructions;

[0055] Step 5: The sorting device uses the telescopic joint 3, bushing 4, and sliding rotating mechanism 5 to move left and right to adjust its position. The sorting assembly, consisting of XOY axis 112, XOZ motor 13, XOZ axis 14, XOZ arm 15, XOY axis 216, electric telescopic rod 17, pin 18, bearing 19, gripper 20, connecting block 21, and locking nut 22, performs the sorting operation. It follows the control instructions from Step 4 to perform the sorting operation until the sorting is completed. When the pressure signal in sorting area 2 becomes 0, the next operation can proceed.

[0056] Step 6: When the pressure signal of sorting area 2 becomes 0, it means that there are no more aviation products in the sorting area 2. The pressure signal will trigger the IoT switch 28. The IoT switch 28 controls the aviation product classification and screening device to stop working through the communication module 29 and records in place of the recording button 11. The recording work can also be recorded manually by pressing the recording button 11.

[0057] Step 7: When the pressure signal in sorting area 2 is greater than 0, continue to execute steps 3, 4, 5 and 6 to classify, screen and sort aviation products until the sorting work is completed and the next operation is performed.

[0058] Step 8: Press the start button 10 to turn off the sorting and screening device, return it to its original position, and complete the sorting and screening of aviation products.

[0059] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any modifications or substitutions made by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A deep learning-based aviation product classification and screening device, characterized in that, It consists of a mechanical structure, a sorting and identification structure, and a control structure; The mechanical structure consists of a display screen (1), a sorting area (2), connecting screws (6), a mouse (8), and a keyboard (9); The device consists of an XOY axis rotating shaft 1 (12), an XOZ motor (13), an XOZ axis rotating shaft (14), an XOZ arm rotating arm (15), an XOY axis rotating shaft 2 (16), an electric telescopic rod (17), a pin (18), a bearing (19), a clamp (20), a connecting block (21), and a locking nut (22). The control structure consists of a telescopic device (3), a bushing (4), a sliding rotating mechanism (5), a slide rail (7), a start button (10), a record button (11), a deep learning camera (23), a signal acquisition module (24), a signal conditioning module (25), a signal processing module (26), a signal control module (27), an IoT gateway (28), and a communication module (29).

2. The aviation product classification and screening device based on deep learning according to claim 1, characterized in that, The display screen (1) is a display component used to display the operation interface and classification and filtering results, so that operators can monitor the filtering results and perform emergency operations in real time. The sorting area (2) is a storage component for screening parts. The sorting area (2) is responsible for receiving aviation products to be screened, classifying them according to preset classification standards, and using a device to identify and sort the parts in the sorting area to the storage area. Each sorting area is equipped with a pressure sensor. When a part to be sorted is placed on the sorting area (2), a pressure signal is generated for use by the Internet of Things gateway (28).

3. The aviation product classification and screening device based on deep learning according to claim 2, characterized in that, The connecting screw (6) is a connecting component. The connecting screw (6) is used to connect the slide rail (7) to the sorting area (2) to ensure a stable connection and smooth sliding between the components, thereby improving the stability and durability of the entire device. The mouse (8) and keyboard (9) are input device components. By modifying the recognition algorithm through the mouse (8) and keyboard (9), the position of one picking area and five storage areas can be controlled to adapt to different environmental conditions. The mouse (8) and keyboard (9) also facilitate operators to input instructions and parameters to control the entire classification and screening process.

4. The aviation product classification and screening device based on deep learning according to claim 3, characterized in that, The XOY axis 1 (12), XOZ motor (13), XOZ axis (14), XOZ arm (15) and XOY axis 2 (16) are transmission components. They together form a multi-dimensional rotation mechanism. Through their relative movement relationship, the electric telescopic rod (17) drives the deep learning camera (23) to take pictures and identify aviation products from different angles, thereby improving the accuracy and comprehensiveness of the identification.

5. The aviation product classification and screening device based on deep learning according to claim 4, characterized in that, The electric telescopic rod (17), pin (18), bearing (19), gripper (20), connecting block (21) and locking nut (22) are moving action components. The gripper (20) and connecting block (21) are fixed by the pin (18) and bearing (19). The bearing (19) is interference-fitted to the gripper (20) and connecting block (21) respectively. The interference-fitted connection between the pin (18) and bearing (19) prevents movement and prevents it from moving in the Z direction. It can only move in the XOY plane. The working action of the gripper (20) is realized by the ingenious structure. When working, the locking nut (22) is used to fix the connecting block (21) on the connecting block (21) so that it cannot produce relative displacement. The extension and retraction of the electric telescopic rod (17) are controlled to realize the clamping and releasing of the gripper (20) and realize the gripping function of the parts to be sorted.

6. The aviation product classification and screening device based on deep learning according to claim 5, characterized in that, The telescopic device (3), bushing (4), sliding turntable (5), and slide rail (7) are displacement components. The telescopic shaft of the telescopic device (3) passes through the bushing (4) and is protected by the bushing (4). The telescopic shaft of the telescopic device (3) is connected to the sliding turntable (5) and can transmit the telescopic displacement movement of the telescopic device (3) to the sliding turntable (5). Through the cooperation of these components, the extension and retraction of the sorting arm can be realized, ensuring that the sorting and identification structure can be accurately delivered to the designated position by displacement action, thus realizing the movement of the entire identification and sorting component.

7. The aviation product classification and screening device based on deep learning according to claim 6, characterized in that, The start button (10) and record button (11) are control components, used to start the classification and screening program and record the screening results, respectively, to facilitate subsequent data analysis and processing.

8. The aviation product classification and screening device based on deep learning according to claim 7, characterized in that, The deep learning camera (23) is a component for recognizing information. As a core recognition component, it can capture image information of aviation products in real time and perform preprocessing and analysis through the signal acquisition module (24), signal conditioning module (25), and signal processing module (26).

9. The aviation product classification and screening device based on deep learning according to claim 8, characterized in that, The signal acquisition module (24), signal conditioning module (25), signal processing module (26), and signal control module (27) are signal acquisition, conditioning, processing, and control components. The signal acquisition module (24) can acquire signals identified by the deep learning camera (23). The acquired signals are conditioned and processed by the signal conditioning module (25) and transformed into signals that can be received by the signal processing module (26). The signal processing module (26) receives and processes the signals, derives control commands, and transmits them to the signal control module (27). The signal control module (27) issues execution actions for the control commands, thereby realizing the functions of signal acquisition, conditioning, processing, and control.

10. The aviation product classification and screening device based on deep learning according to claim 9, characterized in that, The IoT gate (28) and communication module (29) are remote control functional components. When the pressure signal of the sorting area (2) is transmitted to the IoT gate (28), the IoT gate (28) can control the start of the device through the communication module (29) to work instead of the start button (10). When the pressure is too high, the upper limit threshold of the IoT gate (28) will be triggered, and a warning message will be displayed on the display screen (1), indicating that the sorting area cannot be divided into blocks and no more parts can be placed. The sorting identification structure will no longer send sorted parts to the storage area blocks.

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