Electronic machine learning device
Through the combined structure of worm, worm gear and threaded rod, the problem of inconvenience in adjusting angles and fixing of existing electronic machine learning devices is solved, flexible angle adjustment and convenient clamping are achieved, and the applicability and convenience of the learning device are improved.
Patent Information
- Application Number
- CN202422591882.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Utility models(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-25
- Publication Date
- 2025-07-25
- Estimated Expiration
- 2034-10-25
AI Technical Summary
The existing electronic machine learning devices are not convenient to adjust the learning viewing angle, are low in flexibility, and are not convenient to easily fix and remove different devices.
The combined structure of worm, worm gear and threaded rod is adopted. The worm gear and worm gear are driven to rotate by rotating the shaking handle to realize the angle adjustment of the fixed plate, and the bidirectional screw and slider structure are used to achieve convenient clamping and removal of the clamp.
It realizes flexible angle adjustment and convenient fixing of electronic machine learning devices, which are suitable for different learning angle requirements, and improves the flexibility and convenience of use.
Smart Images
Figure CN223153237U_ABST
Abstract
Description
Technical Field
[0001] The utility model relates to the technical field of electronic learning devices, in particular to an electronic machine learning device. Background Art
[0002] Nowadays, there are various electronic products. With the development of technology, electronic machines have also been widely applied to learning, resulting in many electronic machine learning devices, such as language repeaters, point readers, and learning tablets. These products can assist students in learning and reduce the learning burden of students. However, the existing electronic machine learning devices are not convenient for adjusting the learning viewing angle, with low flexibility, unable to meet different learning angle requirements, and not convenient for conveniently fixing and removing different electronic machine learning devices, making the device have certain deficiencies. Content of the Utility Model
[0003] The purpose of the utility model is to solve the deficiencies existing in the prior art and propose an electronic machine learning device.
[0004] To achieve the above purpose, the utility model adopts the following technical scheme: an electronic machine learning device, including a support bottom plate. A fixing block and a fixing frame are fixedly connected to the top of the support bottom plate. A first chute is opened in the fixing frame. A worm is rotatably connected to the top of the support bottom plate. A first fixing rod is rotatably connected to the side of the fixing frame. A worm gear is fixedly connected to the middle of the first fixing rod. The worm gear meshes with the worm. One end of the first fixing rod passes through the fixing frame and is fixedly connected to a threaded rod. A first fixing plate is threadedly connected to the outside of the threaded rod. Two fixing rods are fixedly connected to both sides of the first fixing plate. The two fixing rods slide in the first chute. A first connecting plate is rotatably connected to the outside of the two fixing rods. A second fixing plate is rotatably connected to the side of the fixing block. The second fixing plate is rotatably connected to the first connecting plate.
[0005] As a further description of the above technical solution:
[0006] A second connecting plate is fixedly connected to the top surface of the second fixing plate. A third fixing plate is fixedly connected to the top of the second connecting plate. A second chute is opened in the third fixing plate. A bidirectional lead screw is rotatably connected in the second chute. Sliders are threadedly connected to both sides of the bidirectional lead screw. The sliders slide in the second chute.
[0007] As a further description of the above technical solution:
[0008] A rotating handle is fixedly connected to one end of the bidirectional lead screw.
[0009] As a further description of the above technical solution:
[0010] A clamping plate is fixedly connected to the top of the slider. A sponge pad is fixedly connected to the side of the clamping plate.
[0011] As a further description of the above technical solution:
[0012] One end of the second fixed rod away from the fixed frame is fixedly connected with a limiting plate.
[0013] As a further description of the above technical solution:
[0014] A crank is fixedly connected to the top end of the worm.
[0015] As a further description of the above technical solution:
[0016] Two groups of the first connecting plates are symmetrically arranged.
[0017] The utility model has the following beneficial effects:
[0018] 1. In the utility model, rotating the crank drives the worm to rotate, drives the worm gear to rotate, thereby drives the threaded rod to rotate, drives the first fixing plate to move, and the second fixed rod slides in the first chute, drives the first connecting plate to rotate, and enables the second fixing plate to rotate around the fixed block by an angle, can adjust the angle of the electronic machine learning device for learning and viewing, with high flexibility and suitable for different learning angle requirements.
[0019] 2. In the utility model, rotating the rotating handle drives the bidirectional lead screw to rotate, drives the sliders on both sides to move inwards, and enables the clamping plates with sponge pads to conveniently clamp and remove different electronic machine learning devices. Description of the Drawings
[0020] Figure 1 is a schematic diagram of the overall structure of an electronic machine learning device proposed by the utility model;
[0021] Figure 2 is an enlarged view of part A in an electronic machine learning device proposed by the utility model Figure 1 ;
[0022] Figure 3 is a top view of an electronic machine learning device proposed by the utility model.
[0023] Legend:
[0024] 1. Support base plate; 2. Fixed block; 3. Fixed frame; 4. First chute; 5. Worm; 6. Crank; 7. First fixed rod; 8. Worm gear; 9. Threaded rod; 10. First fixing plate; 11. Second fixed rod; 12. Limiting plate; 13. First connecting plate; 14. Second fixing plate; 15. Second connecting plate; 16. Third fixing plate; 17. Second chute; 18. Rotating handle; 19. Bidirectional lead screw; 20. Slider; 21. Clamping plate; 22. Sponge pad. Detailed Embodiment
[0025] 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. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.
[0026] Referring to Figures 1 - 3 , an embodiment provided by the present invention: an electronic machine learning device, including a support base plate 1, a fixed block 2 and a fixed frame 3 are fixedly connected to the top of the support base plate 1, a first chute 4 is opened in the fixed frame 3, a worm 5 is rotatably connected to the top of the support base plate 1, a first fixed rod 7 is rotatably connected to the side of the fixed frame 3, a worm gear 8 is fixedly connected to the middle of the first fixed rod 7, the worm gear 8 meshes with the worm 5, one end of the first fixed rod 7 passes through the fixed frame 3 and is fixedly connected to a threaded rod 9, a first fixing plate 10 is threadedly connected to the outside of the threaded rod 9, second fixed rods 11 are fixedly connected to both sides of the first fixing plate 10, the second fixed rods 11 slide in the first chute 4, a first connecting plate 13 is rotatably connected to the outside of the second fixed rods 11, a second fixing plate 14 is rotatably connected to the side of the fixed block 2, and the second fixing plate 14 is rotatably connected to the first connecting plate 13, which is convenient for adjusting the angle of the second fixing plate 14.
[0027] A second connecting plate 15 is fixedly connected to the top surface of the second fixing plate 14, a third fixing plate 16 is fixedly connected to the top of the second connecting plate 15, a second chute 17 is opened in the third fixing plate 16, a bidirectional lead screw 19 is rotatably connected in the second chute 17, sliders 20 are threadedly connected to both sides of the bidirectional lead screw 19, the sliders 20 slide in the second chute 17, a rotary handle 18 is fixedly connected to one end of the bidirectional lead screw 19, a clamping plate 21 is fixedly connected to the top of the slider 20, a sponge pad 22 is fixedly connected to the side of the clamping plate 21, a limiting plate 12 is fixedly connected to the end of the second fixed rod 11 away from the fixed frame 3, a crank 6 is fixedly connected to the top end of the worm 5, and two groups of the first connecting plates 13 are symmetrically arranged, making the rotation of the second fixing plate 14 more stable.
[0028] Working principle: Place the electronic machine learning device on the fixing plate three 16. Rotate the rotating handle 18 to drive the bidirectional lead screw 19 to rotate, so that the bidirectional lead screw 19 drives the sliders 20 on both sides to slide in the chute two 17, driving the sliders 20 on both sides to move inward, so that the clamping plates 21 with sponge pads 22 can conveniently clamp different electronic machine learning devices; Rotate the crank 6 to drive the worm 5 to rotate, the worm 5 drives the worm wheel 8 to rotate, the worm wheel 8 drives the fixing rod one 7 to rotate, the fixing rod one 7 drives the threaded rod 9 to rotate, the threaded rod 9 drives the fixing plate one 10 to move, and at the same time drives the fixing rods two 11 on both sides to slide in the chute one 4, driving the connecting plate one 13 to rotate, thereby driving the fixing plate two 14 to rotate around the fixed block 2 by an angle, which can adjust the angle of learning and viewing the electronic machine learning device, with high flexibility and suitable for different learning angle requirements.
[0029] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not used to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, for those skilled in the art, they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. An electronic machine learning device, comprising a support base plate (1), characterized in that: A fixed block (2) and a fixed frame (3) are fixedly connected to the top of the support bottom plate (1). A first chute (4) is formed in the fixed frame (3). A worm (5) is rotatably connected to the top of the support bottom plate (1). A first fixed rod (7) is rotatably connected to the side of the fixed frame (3). A worm gear (8) is fixedly connected to the middle of the first fixed rod (7). The worm gear (8) is engaged with the worm (5). One end of the first fixed rod (7) passes through the fixed frame (3) and is fixedly connected to a threaded rod (9). A first fixing plate (10) is threadedly connected to the outside of the threaded rod (9). Second fixed rods (11) are fixedly connected to both sides of the first fixing plate (10). The second fixed rods (11) slide in the first chute (4). A first connecting plate (13) is rotatably connected to the outside of the second fixed rods (11). A second fixing plate (14) is rotatably connected to the side of the fixed block (2). The second fixing plate (14) is rotatably connected to the first connecting plate (13).
2. An electronic machine learning device according to claim 1, characterized in that: A second connecting plate (15) is fixedly connected to the top surface of the second fixing plate (14). A third fixing plate (16) is fixedly connected to the top of the second connecting plate (15). A second chute (17) is formed in the third fixing plate (16). A bidirectional lead screw (19) is rotatably connected in the second chute (17). Sliders (20) are threadedly connected to both sides of the bidirectional lead screw (19). The sliders (20) slide in the second chute (17).
3. An electronic machine learning device according to claim 2, characterized in that: A rotary handle (18) is fixedly connected to one end of the bidirectional lead screw (19).
4. An electronic machine learning device according to claim 2, characterized in that: A clamping plate (21) is fixedly connected to the top of the slider (20). A sponge pad (22) is fixedly connected to the side of the clamping plate (21).
5. An electronic machine learning device according to claim 1, characterized in that: A limiting plate (12) is fixedly connected to the end of the second fixed rod (11) away from the fixed frame (3).
6. An electronic machine learning device according to claim 1, characterized in that: A rocking handle (6) is fixedly connected to the top end of the worm (5).
7. An electronic machine learning device according to claim 1, characterized in that: Two groups of the first connecting plates (13) are symmetrically arranged.