Intelligent verification device for automatic tool changing of numerical control machining center

Abstract

The invention relates to the technical field of verification of tool selection before aeronautical part numerical control machining, in particular to an intelligent verification device for automatic tool changing of a numerical control machining center, which combines a deep learning technology and an internet of things technology to realize verification detection of tool changing of the numerical control machining center. The accuracy and reliability of tool selection are improved, and quality loss caused by tool problems is avoided.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of verification of tool selection for numerical control machining of aviation parts, in particular to an intelligent verification device for automatic tool changing of a numerical control machining center. The device combines deep learning technology with Internet of Things technology to realize verification and detection of tool changing of the numerical control machining center, improve the accuracy and reliability of tool selection, and avoid quality loss caused by tool problems. BACKGROUND

[0002] In the field of numerical control machining, the selection of tools is crucial for machining quality and efficiency. Traditional tool selection verification methods mainly rely on manual operation and experience-based judgment, which is not only inefficient but also prone to errors. With the development of aviation manufacturing technology, the machining precision and quality of parts are increasingly demanding, and traditional tool verification methods have been unable to meet the needs of modern numerical control machining. In addition, although existing numerical control machining centers have automatic tool changing functions, they lack intelligent verification mechanisms during tool changing, which cannot ensure the accuracy and reliability of selected tools. Once the tool selection is inappropriate, it will not only lead to a decline in machining quality, but also may cause serious safety accidents.

[0003] To address the above problems, although there are some auxiliary tools for tool verification on the market, these tools are mostly single-function and can only perform simple tool size measurement or material identification, and cannot achieve comprehensive intelligent verification. At the same time, these tools are often incompatible with the automatic tool changing system of numerical control machining centers, and require manual operation multiple times to complete the verification process, which undoubtedly increases the complexity and time cost of operation. Therefore, it is particularly important to develop a device that can seamlessly interface with the automatic tool changing system of numerical control machining centers to achieve intelligent and automated tool verification, thereby improving the machining quality and efficiency of numerical control machining centers. SUMMARY

[0004] To solve the above problems, the present application provides an intelligent verification device for automatic tool changing of a numerical control machining center to solve the problems raised in the background art. The device uses deep learning algorithms to train and learn tools, and performs deep learning identification on selected tools to ensure that the selected tools are consistent with the required tools. The device also innovatively introduces an Internet of Things gateway to connect the intelligent verification device and the numerical control machining center using communication technology. In the event of a selection error, the device controls the numerical control machining center to stop processing to prevent unnecessary losses. Furthermore, the device uses an intelligent verification device to eliminate the shortcomings of manual verification, effectively improving verification efficiency and accuracy.

[0005] To solve the above problems, the present application provides an intelligent verification device for automatic tool changing of a numerical control machining center to solve the problems raised in the background art.

[0006] The technical problem solved by the present application is implemented by the following technical scheme:

[0007] In the present application, the deep learning recognition device is used to recognize the called tool before machining of the numerical control machining center, and the recognition signal is transmitted to the numerical control machining center host computer through the Internet gateway, the tool calling signal in the host computer is compared with the recognition signal, the consistency of the actual tool selection and the host computer calling information is verified, the green light is flickered to prompt the requirement under the consistent condition, the red light is flickered and the sound is emitted to prompt the requirement under the inconsistent condition, manual tool verification is required, the error tool is prevented from flowing into the machining process to cause scrap loss, and the product delivery progress is more affected.

[0008] According to one aspect of the present application, an intelligent verification device for automatic tool changing of a numerical control machining center is provided, which is composed of a mechanical structure, a deep learning recognition structure and a function implementation structure.

[0009] The mechanical structure mainly comprises a joystick 1, a screw rod 2, a pressing head 3, a support body 4, a switch button 5, an HTTP port 8, a power supply system 9, a connecting stud 10, a rotating shaft 11 and a protective cover 24.

[0010] The deep learning recognition structure mainly comprises a telescopic rod support body 12, an electric telescopic device 13, a telescopic rod 14, a camera support body 15, a camera connecting shaft 16, a camera module 17, a micro telescopic device 20 and an identification body 25.

[0011] The function implementation structure mainly comprises a light alarm 6, a sound alarm 7, a signal conversion module 18, a deep learning visual computing module 19, a control module 21, a host computer 22 and an Internet gateway 23.

[0012] The joystick 1, the screw rod 2 and the pressing head 3 are main pressing structure components, the joystick 1 and the screw rod 2 are connected by interference, the tight connection of the joystick 1 and the screw rod 2 is realized, the screw rod 2 can rotate with the joystick 1, the screw rod 2 has an external thread, the connection between the screw rod 2 and the support body 4 is realized through the external thread, and the movement of the screw rod 2 is realized through the relative movement of the screw rod 2 and the support body 4, so that the pressing head 3 is conveniently moved to complete the connection and fixation, one end of the pressing head 3 is connected with the screw rod 2 through a ball hinge, the ball hinge connection is more flexible, the pressing head 3 can be rotated through the ball hinge connection, and the connection on the numerical control machining center is more tightly adjusted.

[0013] The support body 4 is a main bearing member, the support body 4 is locked on the numerical control machining center by the joint action of the joystick 1, the screw rod 2 and the pressing head 3, and stable support is provided for the mechanical structure part of the whole invention device, so that the recognition function is better realized.

[0014] The switch button 5 is a standby opening and closing member. The device can be automatically started with the start of the numerical control machining center, and automatically closed with the closing. The switch button 5 is a member for manual starting and closing when the automatic starting or automatic closing of the device fails, preventing the device from failing to start automatically and missing the detection of tool changing, and improving the reliability of the device.

[0015] The HTTP port 8 is an algorithm upgrade access member, fixedly connected to one side of the protective cover 24. The HTTP port 8 can import an identification algorithm to realize iteration of the identification algorithm, meet different application requirements, and be used for connecting a computer for programming interaction, achieving multiple connection input purposes.

[0016] The power supply system 9 is a power supply member, including a charging port, a charging module and a power module. The power module is charged by the charging module through the charging port, and the power stored in the power module is used for power supply of the identification device.

[0017] The connecting stud 10 is a connecting member in the form of a double-headed stud. One end of the connecting stud 10 is connected to the support body 4, and the other end is connected to the rotating shaft 11, which serves to connect and fix the device and the identification device.

[0018] The rotating shaft 11 is a bearing member for bearing the identification member. One end of the rotating shaft 11 is internally threaded and connected to the external thread of the connecting stud 10, which serves to bear the identification member. The protective cover 24 is a protective member connected to the support body 4, which protects the internal members and fixes the switch button 5, the light alarm 6 and the sound alarm 7 thereon by bolt connection, serving to support them.

[0019] The telescopic rod support body 12 is a support member of the deep learning identification structure, which is bolted to the identification body 25 and fixed on the identification body 25, and can fix the electric telescopic rod 13, serving to support and fix the electric telescopic rod 13. As shown in the figure, the telescopic rod support body 12 is composed of four support parts, which can bear four electric telescopic rods 13 and realize multi-angle adjustment of the camera support body 15.

[0020] The electric telescopic rod 13 and the telescopic rod 14 are action execution members. The telescopic rod 14 is inside the electric telescopic rod 13, which drives the telescopic rod 14 to extend, serving to adjust the position of the telescopic rod 14 and further adjust the camera support body 15.

[0021] The camera support body 15 is an indirect support member, one end of which is connected to the telescopic rod 14 and the other end is connected to the camera connecting shaft 16. The camera support body 15 indirectly connects the telescopic rod 14 and the camera connecting shaft 16, and the telescopic rod 14 is arranged to adjust the camera connecting shaft 16.

[0022] The camera connecting shaft 16 is a motion transmission component, used to realize the transmission of motion between the electric telescopic device 13 and the telescopic rod 14 to the micro-motion telescopic device 20, and plays the role of connection and transmission; the camera module 17 is a signal acquisition component, which can be used to acquire images of the cutting tools used by the CNC machining center and transmit the signals to the signal conversion module 18, thus playing the role of image data acquisition.

[0023] The micro-adjustment telescopic device 20 is a fine-tuning component. Under the adjustment of the electric telescopic device 13 and the telescopic rod 14, it has a preliminary tool recognition function. Its clarity and other properties may be deployed to the best state. At this time, the micro-adjustment telescopic device 20 is needed to make fine adjustments to the camera module 17 to make the acquired image data clearer and more distinguishable.

[0024] The identification body 25 is the load-bearing and protective component of the deep learning identification structure of this method. The deep learning identification structure is located inside the identification body 25, which plays a protective role for these components.

[0025] The light alarm 6 and sound alarm 7 are warning components. When the actual selection of the tool is inconsistent with the required selection, a flashing red light and a beeping sound will be emitted as a warning. When the actual selection of the tool is consistent with the required selection, a flashing green light will be emitted as a reminder.

[0026] The signal conversion module 18 is a signal receiving, conversion and transmission component. It can convert the image signal recognized by the camera module 17 into a signal that can be recognized and processed by the deep learning visual computing module 19, thus playing the role of signal conversion.

[0027] The deep learning visual computing module 19 is a data processing component. The deep learning visual computing module 19 can process the signal transmitted by the signal conversion module 18, and compare it with the tool signal input by the host computer 22 through the Internet of Things gateway 23 to obtain the verification conclusion of tool selection.

[0028] The control module 21 is a control component. After the deep learning vision computing module 19 obtains the verification conclusion of the tool selection, the control module 21 drives the light alarm 6 and the sound alarm 7 to perform corresponding actions, thus playing the role of the control system.

[0029] The host computer 22 is a demand-providing component. It reads the tool requirements provided by the CNC machining center's machining program and indirectly transmits them to the deep learning vision computing module 19 via the IoT gateway 23. The deep learning vision computing module 19 determines the consistency between the actual tool selection and the required tool selection, thus providing the required tool signal. The IoT gateway 23 is an IoT switch component. By setting it, the signals between the host computer 22 and the deep learning vision computing module 19 can be controlled, triggering information comparison actions to control signals that are not interconnected.

[0030] The advantages of this application are:

[0031] The intelligent verification device for automatic tool changing in CNC machining centers has achieved significant results in practical applications. Firstly, the device greatly improves the accuracy and efficiency of automatic tool changing in CNC machining centers. Through the intelligent verification system, the status and position of the tool can be detected in real time, ensuring the precision of each tool change operation. This not only reduces machining errors caused by incorrect tool changes but also improves overall production efficiency. Furthermore, the intelligent verification device can analyze tool wear through advanced algorithms, enabling preventative tool replacement before potential problems arise, further guaranteeing machining quality.

[0032] Secondly, the device boasts a high level of automation and intelligence. By integrating a deep learning vision computing module and a control module, it achieves autonomous monitoring and management of the tool changing process. This not only reduces the need for manual intervention but also increases the automation level of the production line, saving labor costs for enterprises. Furthermore, the device possesses reliable safety features. With the installation of light and sound alarms, it can promptly issue alerts when abnormal situations are detected, reminding operators to take action. Simultaneously, the control module can take corresponding protective measures according to preset safety policies to ensure the safety of equipment and personnel. In emergencies, the device can quickly cut off the power supply to prevent potential accidents, providing additional safety assurance for operators.

[0033] In conclusion, this intelligent verification device for automatic tool changing in CNC machining centers has achieved excellent results in practical applications. It not only improves production efficiency and accuracy but also enhances the automation and intelligence level of the production line. By reducing human error and optimizing the production process, it brings significant economic benefits and increased production efficiency to enterprises, while also providing operators with a safer and more comfortable working environment. Attached Figure Description

[0034] Figure 1 It is an isometric drawing of an intelligent verification device for automatic tool changing in CNC machining centers;

[0035] Figure 2This is the front view of an intelligent verification device for automatic tool changing in CNC machining centers;

[0036] Figure 3 This is a top view of an intelligent verification device for automatic tool changing in CNC machining centers;

[0037] Figure 4 This is a cross-sectional view of the identifier of an intelligent verification device used for automatic tool changing in CNC machining centers;

[0038] Figure 5 This is a signal control diagram for an intelligent verification device used for automatic tool changing in CNC machining centers.

[0039] The components include: 1. joystick; 2. screw rod; 3. clamping head; 4. support body; 5. switch button; 6. light alarm; 7. sound alarm; 8. HTTP port; 9. power system; 10. connecting stud; 11. shaft; 12. telescopic rod support body; 13. electric telescopic device; 14. telescopic rod; 15. camera support body; 16. camera connecting shaft; 17. camera module; 18. signal conversion module; 19. deep learning visual computing module; 20. micro-motion telescopic device; 21. control module; 22. host computer; 23. IoT gateway; 24. protective cover; and 25. identification device. Detailed Implementation

[0040] The present application is described in detail below with reference to the embodiments, but the present application is not limited to these embodiments.

[0041] Example 1

[0042] An intelligent verification device for automatic tool changing in CNC machining centers consists of a mechanical structure, a deep learning recognition structure, and a function implementation structure.

[0043] The mechanical structure mainly consists of a control lever 1, a screw rod 2, a clamping head 3, a support body 4, a switch button 5, an HTTP port 8, a power system 9, a connecting stud 10, a rotating shaft 11, and a protective cover 24.

[0044] The deep learning recognition structure mainly consists of a telescopic rod support 12, an electric telescopic device 13, a telescopic rod 14, a camera support 15, a camera connecting shaft 16, a camera module 17, a micro-motion telescopic device 20, and a recognition body 25.

[0045] The functional implementation structure mainly consists of a light alarm 6, a sound alarm 7, a signal conversion module 18, a deep learning visual computing module 19, a control module 21, a host computer 22, and an IoT gateway 23.

[0046] The control lever 1, the screw rod 2, and the clamping head 3 are the main clamping structural components. The control lever 1 and the screw rod 2 are connected by an interference fit, which allows the screw rod 2 to rotate together with the control lever 1. The screw rod 2 has its own external thread, which connects to the support body 4. The relative movement of the screw rod 2 and the support body 4 enables the movement of the screw rod 2, which conveniently drives the clamping head 3 to move, thus completing the connection and fixation function. One end of the clamping head 3 is connected to the screw rod 2 by a ball joint. The ball joint connection provides more flexibility and allows the clamping head 3 to rotate on its own. By adjusting the direction, the connection on the CNC machining center can be made more secure.

[0047] The support body 4 is the main load-bearing component. The control lever 1, the screw rod 2 and the clamping head 3 work together to lock the support body 4 on the CNC machining center, providing stable support for the mechanical structure of the entire invention device, so as to better realize the recognition function.

[0048] The switch button 5 is a backup start and stop component. The device can start automatically when the CNC machining center starts and stop automatically when it stops. The switch button 5 is a component for manual start and stop when the device fails to start or stop automatically, so as to prevent the tool change verification from being missed due to the failure of the device to start automatically, and improve the reliability of the device.

[0049] The HTTP port 8 is an algorithm upgrade access component, which is fixedly connected to one side of the protective cover 24. The recognition algorithm can be imported through the HTTP port 8 to realize the iteration of the recognition algorithm, meet different application requirements, and can be used to connect to a computer for programming interaction, achieving multiple connection input purposes.

[0050] The power system 9 is a power supply component, including a charging port, a charging module, and a power module. The power module is charged through the charging port using the charging module, and the power stored in the power module is used to identify the power supply of the device.

[0051] The connecting stud 10 is a connecting component, which adopts the form of a double-ended stud. One end of the connecting stud 10 is connected to the support body 4, and the other end is connected to the rotating shaft 11, which serves to connect the fixing device and the identification device.

[0052] The rotating shaft 11 is a load-bearing component used to support the identification component. One end of it has an internal thread that connects to the external thread of the connecting stud 10, thus supporting the identification component. The protective cover 24 is a protective component connected to the support body 4, which protects the internal components. The switch button 5, the light alarm 6, and the sound alarm 7 are fixed on it by bolts, thus supporting them.

[0053] The telescopic rod support 12 is a support component of the deep learning recognition structure. It is bolted to the recognition body 25 and fixed to the recognition body 25. It can also be used to fix the electric telescopic device 13, thus providing support and fixation for the electric telescopic device 13. As shown in the figure, the telescopic rod support 12 consists of four sets of support parts, which can support four sets of electric telescopic devices 13 and enable multi-angle adjustment of the camera support 15.

[0054] The electric telescopic device 13 and the telescopic rod 14 are the action execution components. The telescopic rod 14 is inside the electric telescopic device 13. The electric telescopic device 13 drives the telescopic rod 14 to extend, so as to adjust the position of the telescopic rod 14, thereby realizing the adjustment of the camera support body 15.

[0055] The camera support 15 is an indirect support component, with one end connected to the telescopic rod 14 and the other end connected to the camera connecting shaft 16. The connection between the telescopic rod 14 and the camera connecting shaft 16 is indirectly achieved through the camera support 15, and the adjustment of the camera connecting shaft 16 is achieved by using the telescopic rod 14.

[0056] The camera connecting shaft 16 is a motion transmission component, used to realize the transmission of motion between the electric telescopic device 13 and the telescopic rod 14 to the micro-motion telescopic device 20, and plays the role of connection and transmission; the camera module 17 is a signal acquisition component, which can be used to acquire images of the cutting tools used by the CNC machining center and transmit the signals to the signal conversion module 18, thus playing the role of image data acquisition.

[0057] The micro-adjustment telescopic device 20 is a fine-tuning component. Under the adjustment of the electric telescopic device 13 and the telescopic rod 14, it has a preliminary tool recognition function. Its clarity and other properties may be deployed to the best state. At this time, the micro-adjustment telescopic device 20 is needed to make fine adjustments to the camera module 17 to make the acquired image data clearer and more distinguishable.

[0058] The identification body 25 is the load-bearing and protective component of the deep learning identification structure of this method. The deep learning identification structure is located inside the identification body 25, which plays a protective role for these components.

[0059] The light alarm 6 and sound alarm 7 are warning components. When the actual selection of the tool is inconsistent with the required selection, a flashing red light and a beeping sound will be emitted as a warning. When the actual selection of the tool is consistent with the required selection, a flashing green light will be emitted as a reminder.

[0060] The signal conversion module 18 is a signal receiving, conversion and transmission component. It can convert the image signal recognized by the camera module 17 into a signal that can be recognized and processed by the deep learning visual computing module 19, thus playing the role of signal conversion.

[0061] The deep learning visual computing module 19 is a data processing component. The deep learning visual computing module 19 can process the signal transmitted by the signal conversion module 18, and compare it with the tool signal input by the host computer 22 through the Internet of Things gateway 23 to obtain the verification conclusion of tool selection.

[0062] The control module 21 is a control component. After the deep learning vision computing module 19 obtains the verification conclusion of the tool selection, the control module 21 drives the light alarm 6 and the sound alarm 7 to perform corresponding actions, thus playing the role of the control system.

[0063] The host computer 22 is a demand-providing component. It reads the tool requirements provided by the CNC machining center's machining program and indirectly transmits them to the deep learning vision computing module 19 via the IoT gateway 23. The deep learning vision computing module 19 determines the consistency between the actual tool selection and the required tool selection, thus providing the required tool signal. The IoT gateway 23 is an IoT switch component. By setting it, the signals between the host computer 22 and the deep learning vision computing module 19 can be controlled, triggering information comparison actions to control signals that are not interconnected.

[0064] The specific work process is as follows:

[0065] Step 1: Before use, perform a visual inspection of the intelligent verification device for automatic tool changer on the CNC machining center and check whether the device label is complete and the appearance is intact. If it is complete and intact, proceed to the next step.

[0066] Step 2: Start the CNC machining center and trigger the IoT switch 23. At this time, the intelligent verification device will be activated. If it is not activated, manually press the switch button 5 to complete the start-up. Check the intelligent verification...

[0067] Verify the continuity of the device's circuitry to ensure the intelligent verification device functions properly.

[0068] Step 3: The host computer 22 of the CNC machining center executes the CNC machining program to call the tool, and the CNC machining center calls the tool and picks up the tool according to the instruction;

[0069] Step 4: The intelligent verification device uses the electric telescopic device 13 to control the telescopic rod 14 to adjust the angle of the camera support 15, and the micro-motion telescopic device 20 makes fine adjustments to finally determine the appropriate angle and position of the camera module 17. The camera module 17 is used to extract the features of the picking tool and collect image data.

[0070] Step 5: Check the operation of the light alarm 6 and the sound alarm 7. If the light alarm 6 flashes a "green light" and the sound alarm 7 does not operate, it is determined that the tool calls are consistent. If the light alarm 6 flashes a "red light" and the sound alarm 7 emits a "beep" sound, it is determined that the tool calls are inconsistent and further processing is required.

[0071] Step Six: If the tool selection is inconsistent, after rectification, the intelligent verification process must be repeated from Step Two until the tool selection is consistent.

[0072] Step 7: Turn off the CNC machining center and trigger the IoT switch 23. At this time, the intelligent verification device will turn off. If it is not turned off, manually press the switch button 5 to complete the shutdown.

[0073] 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. An intelligent verification device for automatic tool changing in CNC machining centers, characterized in that, It consists of a mechanical structure, a deep learning recognition structure, and a functional implementation structure; The mechanical structure mainly consists of a control lever (1), a screw rod (2), a clamping head (3), a support body (4), a switch button (5), an HTTP port (8), a power system (9), a connecting stud (10), a rotating shaft (11), and a protective cover (24); The deep learning recognition structure mainly consists of a telescopic rod support (12), an electric telescopic device (13), a telescopic rod (14), a camera support (15), a camera connecting shaft (16), a camera module (17), a micro-motion telescopic device (20), and a recognition body (25). The functional implementation structure mainly consists of a light alarm (6), a sound alarm (7), a signal conversion module (18), a deep learning visual computing module (19), a control module (21), a host computer (22), and an Internet of Things gateway (23).

2. The intelligent verification device for automatic tool changing in CNC machining centers according to claim 1, characterized in that, The control lever (1), the screw rod (2), and the clamping head (3) are the main clamping structure components. The control lever (1) and the screw rod (2) are connected by an interference fit to achieve a tight connection between the control lever (1) and the screw rod (2), so that the screw rod (2) can rotate together with the control lever (1). The screw rod (2) has its own external thread, which is connected to the support body (4) through the external thread. The relative movement of the screw rod (2) and the support body (4) realizes the movement of the screw rod (2), which conveniently drives the clamping head (3) to move and completes the connection and fixation function. One end of the clamping head (3) is connected to the screw rod (2) through a ball joint. The ball joint connection is more flexible. The clamping head (3) can rotate by itself through the ball joint connection. The connection is more secure on the CNC machining center by adjusting the direction. The support body (4) is the main load-bearing component. The control lever (1), screw rod (2) and clamping head (3) work together to lock the support body (4) on the CNC machining center, providing stable support for the mechanical structure of the entire invention device, so as to better realize the recognition function.

3. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 2, characterized in that, The switch button (5) is a spare start and stop component. The device can start automatically when the CNC machining center starts and stop automatically when it stops. The switch button (5) is a component for manual start and stop when the device fails to start or stop automatically, so as to prevent the device from failing to start automatically and causing errors or omissions in the tool change verification, thereby improving the reliability of the device. The HTTP port (8) is an algorithm upgrade access component, which is fixedly connected to one side of the protective cover (24). The recognition algorithm can be imported through the HTTP port (8) to realize the iteration of the recognition algorithm, meet different application requirements, and can be used to connect to a computer for programming interaction, so as to achieve multiple connection input purposes. The power system (9) is a power supply component, including a charging port, a charging module and a power module. The power module is charged through the charging port using the charging module. The power stored in the power module is used to identify the power supply of the device.

4. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 3, characterized in that, The connecting stud (10) is a connecting component, which adopts the form of a double-headed stud. One end of the connecting stud (10) is connected to the support body (4), and the other end is connected to the rotating shaft (11), which serves as a connecting and fixing device and an identification device. The rotating shaft (11) is a load-bearing component used to support the identification component. One end of the shaft has an internal thread that connects to the external thread of the connecting stud (10), thus supporting the identification component. The protective cover (24) is a protective component connected to the support body (4) to protect the internal components. The switch button (5), light alarm (6), and sound alarm (7) are fixed on it by bolts, thus supporting them.

5. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 4, characterized in that, The telescopic rod support (12) is a support component of the deep learning recognition structure. It is bolted to the recognition body (25) and fixed on the recognition body (25). It can also be used to fix the electric telescopic device (13) and play a supporting and fixing role for the electric telescopic device (13). As shown in the figure, the telescopic rod support (12) consists of four sets of support parts, which can bear four sets of electric telescopic devices (13) and can realize multi-angle adjustment of the camera support (15). The electric telescopic device (13) and the telescopic rod (14) are the action execution components. The telescopic rod (14) is inside the electric telescopic device (13). The electric telescopic device (13) drives the telescopic rod (14) to extend, so as to adjust the position of the telescopic rod (14) and thus realize the adjustment of the camera support (15).

6. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 5, characterized in that, The camera support (15) is an indirect support component. One end is connected to the telescopic rod (14), and the other end is connected to the camera connecting shaft (16). The connection between the telescopic rod (14) and the camera connecting shaft (16) is indirectly achieved through the camera support (15), and the adjustment of the camera connecting shaft (16) is achieved by using the arranged telescopic rod (14). The camera connecting shaft (16) is a motion transmission component, used to realize the transmission of the movement of the electric telescopic device (13) and the telescopic rod (14) to the micro-motion telescopic device (20), and plays the role of connection and transmission; the camera module (17) is a signal acquisition component, which can be used to acquire images of the tools used by the CNC machining center and transmit the signals to the signal conversion module (18), thus playing the role of image data acquisition.

7. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 6, characterized in that, The micro-motion telescopic device (20) is a fine-tuning component. Under the adjustment of the electric telescopic device (13) and the telescopic rod (14), it has a preliminary tool recognition function. Its clarity and other properties may be deployed in the best state. At this time, the micro-motion telescopic device (20) is needed to make fine adjustments to the camera module (17) to make the acquired image data clearer and more distinguishable. The identification body (25) is the load-bearing and protective component of the deep learning identification structure of this method. The deep learning identification structure is located inside the identification body (25), which plays a protective role for these components.

8. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 7, characterized in that, The light alarm (6) and sound alarm (7) are warning components. When the actual selection of the tool is inconsistent with the required selection, a flashing red light and a beeping sound will be emitted as a warning. When the actual selection of the tool is consistent with the required selection, a flashing green light will be emitted as a reminder. The signal conversion module (18) is a signal receiving, conversion and transmission component. It can convert the image signal recognized by the camera module (17) into a signal that can be recognized and processed by the deep learning visual computing module (19), thus playing the role of signal conversion.

9. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 8, characterized in that, The deep learning visual computing module (19) is a data processing component. The deep learning visual computing module (19) can process the signal transmitted by the signal conversion module (18), and compare it with the tool signal input by the host computer (22) through the Internet of Things gateway (23) to obtain the verification conclusion of tool selection. The control module (21) is a control component. After the deep learning vision computing module (19) obtains the verification conclusion of the tool selection, the control module (21) drives the light alarm (6) and the sound alarm (7) to perform corresponding actions, thus playing the role of the control system.

10. The intelligent verification device for automatic tool changing in a CNC machining center according to claim 9, characterized in that, The host computer (22) is a demand-providing component. The host computer (22) reads the tool requirements provided by the CNC machining center's machining program and indirectly transmits them to the deep learning vision computing module (19) via the Internet of Things gateway (23). The deep learning vision computing module (19) determines the consistency between the actual tool selection and the required tool selection, thus providing the required tool signal. The Internet of Things gateway (23) is an IoT switch component. By setting and controlling the signals between the host computer (22) and the deep learning vision computing module (19), it can trigger information comparison actions and realize the control of non-interoperable signals.

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