An easy-to-assemble deep learning model for graphics processing

Abstract

This utility model discloses an easy-to-assemble graphics processing deep learning model, belonging to the field of learning model technology. The easy-to-assemble graphics processing deep learning model includes an assembly frame and splicing modules arranged in an array inside the assembly frame. The surface of each splicing module has an interlocking groove. The sidewalls of the interlocking groove are symmetrically provided with dovetail blocks and dovetail slots, and two sets of dovetail blocks and dovetail slots are provided respectively. The inner wall of each dovetail slot has a recessed hole, and a spring is installed inside the recessed hole. The end of the spring is connected to a connecting plate that slides through the recessed hole. The surface of the connecting plate away from the spring is connected to a protruding post extending to the outside of the recessed hole, and the end of the protruding post has a hemispherical structure. This allows the splicing module to be limited in both the horizontal and vertical directions, preventing the interlocking parts from separating, and making assembly very convenient.

Description

Technical Field

[0001] This invention relates to the field of learning model technology, and more specifically, to a graphics processing deep learning model that is easy to assemble. Background Technology

[0002] Graph deep learning is a method that applies deep learning techniques to graph data. Graph data is a form of data used to represent relationships and interactions between objects, commonly found in social networks, molecular structures, transportation systems, and recommender systems. Graph deep learning utilizes concepts from graph theory and deep learning techniques to process and learn complex patterns in graphs. A key characteristic of graph data is its topological structure, which reveals complex networks of relationships between nodes. Traditional data analysis methods are difficult to apply directly to graph data; therefore, graph deep learning has unique advantages in processing this type of data.

[0003] Existing deep learning models for graphics processing mainly rely on the assembly of graphics. However, the assembly of existing images mainly uses the interlocking of their edges and the use of dovetail grooves and dovetail blocks. This connection structure can only limit the movement in one direction, which can easily lead to the disengagement of the interlocking parts. After separation, they need to be reassembled, which is very inconvenient. Therefore, we propose a graphics processing deep learning model that is easy to assemble to solve the above problems. Utility Model Content

[0004] 1. Technical problems to be solved

[0005] In view of the problems existing in the prior art, the purpose of this utility model is to provide a graphics processing deep learning model that is easy to assemble. It can limit the splicing modules in both the horizontal and vertical directions, making it difficult for the interlocking parts to separate, and the assembly is very convenient.

[0006] 2. Technical Solution

[0007] To solve the above problems, the present invention adopts the following technical solution.

[0008] A graphics processing deep learning model that is easy to assemble includes an assembly frame and splicing modules arranged in an array inside the assembly frame. The surface of the splicing module is provided with an interlocking groove, and the sidewalls of the interlocking groove are respectively symmetrically provided with dovetail blocks and dovetail slots, and two sets of dovetail blocks and dovetail slots are respectively provided.

[0009] The inner wall of the dovetail slot has a recessed hole, a spring is installed inside the recessed hole, a connecting plate is connected to the end of the spring and is slidably connected to the recessed hole, and a protruding post extending to the outside of the recessed hole is connected to the surface of the connecting plate away from the spring, and the end of the protruding post is set in a hemispherical structure.

[0010] The surface of the dovetail block is provided with a snap-fit ​​groove with a hemispherical structure, and the snap-fit ​​groove and the end of the protrusion form an embedded structure.

[0011] The inner side of the interlocking slot has an embedded structure with graphic images installed.

[0012] Furthermore, the edges of the interlocking groove are symmetrically provided with notches in pairs, and the edges of the graphic image protrude beyond the notches.

[0013] Furthermore, one of the notches is provided with an indicator groove.

[0014] Furthermore, a limiting part with an annular structure is installed at the port of the concave hole, and the limiting part abuts against the connecting plate.

[0015] Furthermore, the root of the arc-shaped surface at one end of the protruding post, which is a hemispherical structure, is located on the same plane as the inner wall of the dovetail groove.

[0016] Furthermore, the dovetail block and the dovetail slot correspond to each other, and the dovetail block and the dovetail slot form an embedded structure.

[0017] Furthermore, the graphic image is made of rigid PVC, and the surface of the graphic image is printed with graphics for splicing.

[0018] 3. Beneficial effects

[0019] Compared with existing technologies, the advantages of this utility model are:

[0020] (1) In this scheme, the graphic images used for deep learning of graphics are placed in the splicing slots opened on the splicing module. Then, according to the composition of the overall graphic, the splicing module with the graphic images is placed on the inner side of the assembly frame to form the overall graphic. Two adjacent splicing modules are connected together by using dovetail clips and dovetail slots. When the dovetail clips and dovetail slots are combined, the hemispherical end of the protrusion is squeezed, thereby using the connecting plate to squeeze and deform the spring located inside the concave hole. After the slot and the protrusion are aligned, the elastic force of the spring is used to make the protrusion and the slot engage, thereby limiting the splicing module in both the horizontal and vertical directions, making it difficult for the engaged parts to separate, and the assembly is very convenient.

[0021] (2) In this solution, the notch is set to facilitate the disassembly of the graphic image installed in the interlocking slot. By setting an indicator slot on one of the notches, the graphic image can be placed in the interlocking slot in a straight position, so that the graphic image is in a consistent position, which is beneficial for the later assembly. Attached Figure Description

[0022] Figure 1 This is a schematic diagram of the splicing module structure of this utility model;

[0023] Figure 2 This is a schematic diagram of the dovetail block and dovetail slot structure of this utility model;

[0024] Figure 3 This is a front view schematic diagram of the splicing module of this utility model;

[0025] Figure 4 This is a cross-sectional view of the splicing module AA of this utility model;

[0026] Figure 5 This is a schematic diagram of the assembly of the splicing module of this utility model.

[0027] Explanation of the labels in the diagram:

[0028] 1. Assembly frame; 2. Assembly module; 3. Interlocking groove; 4. Graphic image; 5. Dovetail block; 6. Dovetail slot; 7. Spring; 8. Recessed hole; 9. Limiting part; 10. Connecting plate; 11. Protruding column; 12. Snap-fit ​​groove; 13. Notch; 14. Indicator groove. Detailed Implementation

[0029] The technical solutions of the present utility model will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present utility model. Obviously, the described embodiments are only some embodiments of the present utility model, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present utility model without creative effort are within the protection scope of the present utility model.

[0030] Example:

[0031] Please see Figure 1-5 A graphics processing deep learning model that is easy to assemble includes an assembly frame 1 and splicing modules 2 arranged in an array inside the assembly frame 1. The surface of the splicing module 2 is provided with a splicing groove 3. The sidewalls of the splicing groove 3 are symmetrically provided with dovetail blocks 5 and dovetail slots 6, and there are two sets of dovetail blocks 5 and dovetail slots 6 respectively.

[0032] The inner wall of the dovetail slot 6 is provided with a recessed hole 8. A spring 7 is installed inside the recessed hole 8. A connecting plate 10 that slides and connects with the recessed hole 8 is connected to the end of the spring 7. A protruding post 11 that extends to the outside of the recessed hole 8 is connected to the surface of the connecting plate 10 away from the spring 7. The end of the protruding post 11 is set in a hemispherical structure.

[0033] The surface of the dovetail block 5 is provided with a snap-fit ​​groove 12 with a hemispherical structure, and the snap-fit ​​groove 12 and the end of the protrusion 11 form an embedded structure.

[0034] The inner side of the interlocking groove 3 has an embedded structure on which graphic image 4 is installed;

[0035] It should be noted that, when using this easily assembled graphics processing deep learning model, the graphics image 4 used for graphics deep learning assembly is placed in the interlocking slot 3 opened on the splicing module 2. Then, according to the overall graphic composition, the splicing module 2 with the graphics image 4 is placed inside the assembly frame 1 to form the overall graphic. Two adjacent splicing modules 2 are connected together by dovetail clips 5 and dovetail slots 6. When the dovetail clips 5 and dovetail slots 6 are combined, the hemispherical end of the protrusion 11 is squeezed, thereby the connecting plate 10 squeezes and deforms the spring 7 located inside the concave hole 8. After the interlocking slot 12 corresponds to the protrusion 11, the elastic force of the spring 7 makes the protrusion 11 and the interlocking slot 12 engage, thereby limiting the splicing module 2 in both the horizontal and vertical directions, making it difficult for the engaged parts to separate, and the assembly is very convenient.

[0036] like Figure 1 , Figure 2 As shown, notches 13 are symmetrically arranged on the edge of the interlocking groove 3, and the edge of the graphic image 4 protrudes from the notches 13. An indicator groove 14 is provided on one of the notches 13.

[0037] It should be noted that by setting notches 13, it is convenient to disassemble the graphic image 4 installed in the interlocking groove 3. By setting an indicator groove 14 on one of the notches 13, the graphic image 4 can be placed upright in the interlocking groove 3, so that the graphic image 4 is in the same position, which is beneficial to the subsequent assembly.

[0038] like Figure 4 As shown, a limiting part 9 with an annular structure is installed at the port of the concave hole 8, and the limiting part 9 abuts against the connecting plate 10.

[0039] It should be noted that the connecting plate 10 serves as a limit to prevent it from dislodging from the recessed hole 8 under the elastic force of the spring 7.

[0040] like Figure 2 , Figure 4As shown, the root of the arc-shaped surface at one end of the protruding post 11, which is a hemispherical structure, is located on the same plane as the inner wall of the dovetail groove 6. The dovetail block 5 and the dovetail groove 6 correspond to each other, and the dovetail block 5 and the dovetail groove 6 form an embedded structure.

[0041] It should be noted that, during the process of the dovetail block 5 and the dovetail groove 6 being connected, the hemispherical arc surface of the protruding post 11 is used to press it into the inner side of the concave hole 8.

[0042] The material of graphic image 4 is rigid PVC, and the surface of graphic image 4 is printed with graphics for splicing.

[0043] In use: Place the graphic image 4 used for deep learning assembly into the interlocking slot 3 on the splicing module 2. Then, according to the overall graphic composition, place the splicing module 2 with the graphic image 4 on the inner side of the assembly frame 1 to form the overall graphic. Connect two adjacent splicing modules 2 together using dovetail clips 5 and dovetail slots 6. When the dovetail clips 5 and dovetail slots 6 are combined, the hemispherical end of the protrusion 11 is squeezed, thereby using the connecting plate 10 to squeeze and deform the spring 7 located inside the concave hole 8. After the interlocking slot 12 corresponds to the protrusion 11, the elastic force of the spring 7 is used to make the protrusion 11 and the interlocking slot 12 engage, and finally the assembly of the overall graphic is completed.

[0044] The above description is merely a preferred embodiment of this utility model; however, the protection scope of this utility model is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the technical scope disclosed in this utility model, based on the technical solution and its improved concept, should be included within the protection scope of this utility model.

Claims

1. A graphics processing deep learning model that is easy to assemble, comprising an assembly frame (1) and splicing modules (2) arranged in an array inside the assembly frame (1), characterized in that: The splicing module (2) has a splicing groove (3) on its surface. The sidewalls of the splicing groove (3) are respectively symmetrically provided with dovetail blocks (5) and dovetail slots (6), and the dovetail blocks (5) and the dovetail slots (6) are respectively provided in two sets. The inner wall of the dovetail groove (6) is provided with a recess (8), and a spring (7) is installed on the inner side of the recess (8). The end of the spring (7) is connected to a connecting plate (10) that is slidably connected to the recess (8). A protruding post (11) extending to the outside of the recess (8) is connected to the surface of the connecting plate (10) away from the spring (7), and the end of the protruding post (11) is provided with a hemispherical structure. The surface of the dovetail block (5) is provided with a snap-fit ​​groove (12) with a hemispherical structure, and the snap-fit ​​groove (12) and the end of the protrusion (11) form an embedded structure. The inner side of the interlocking groove (3) has an embedded structure on which graphic images (4) are installed.

2. The easily assembled graphics processing deep learning model according to claim 1, characterized in that: The edge of the interlocking groove (3) is symmetrically provided with notches (13) in pairs, and the edge of the graphic image (4) protrudes from the notches (13).

3. The easily assembled graphics processing deep learning model according to claim 2, characterized in that: An indicator groove (14) is provided on one of the notches (13).

4. The easily assembled graphics processing deep learning model according to claim 1, characterized in that: The port of the recess (8) is equipped with a limiting part (9) with a ring structure, and the limiting part (9) abuts against the connecting plate (10).

5. The easily assembled graphics processing deep learning model according to claim 1, characterized in that: The root of the arc-shaped surface at one end of the protruding post (11), which is a hemispherical structure, is located on the same plane as the inner wall of the dovetail groove (6).

6. The easily assembled graphics processing deep learning model according to claim 1, characterized in that: The dovetail block (5) and the dovetail slot (6) correspond to each other, and the dovetail block (5) and the dovetail slot (6) form an embedded structure.

7. The easily assembled graphics processing deep learning model according to claim 1, characterized in that: The graphic image (4) is made of rigid PVC, and the surface of the graphic image (4) is printed with graphics for splicing.

Images