An image data processing method, system, terminal and storage medium

Abstract

The application relates to an image data processing method, and relates to the field of image data processing.The method comprises the following steps: acquiring workpiece information; generating feature extraction instructions according to a workpiece conveying image and executing the instructions; generating adjacent vertex connection instructions according to a workpiece convex point feature and executing the instructions; generating structure surface filling instructions according to the workpiece conveying image and executing the instructions; and generating workpiece model marking storage instructions according to a three-dimensional workpiece model and a workpiece number and executing the instructions.The application has the effect of improving the workpiece acceptance accuracy.

Description

Technical Field

[0001] This application relates to the field of image data processing, and in particular to an image data processing method. Background Technology

[0002] Currently, during the production and processing of workpieces, after the workpieces are processed, workpiece acceptance is required. During the transportation of workpieces, they are photographed from multiple angles, and staff conduct workpiece acceptance based on the photographed images.

[0003] Regarding the aforementioned technologies, when staff inspect workpiece images, the images are image data from the workpiece transportation process. The workpiece images are in motion and the workpiece moves at a relatively high speed. It is difficult for staff to identify the workpiece images during the transportation process, which can easily lead to misjudgments and a low accuracy rate in workpiece inspection. Summary of the Invention

[0004] To improve the accuracy of workpiece acceptance, this application provides an image data processing method.

[0005] In a first aspect, this application provides an image data processing method, which adopts the following technical solution: An image data processing method includes acquiring workpiece information, which includes workpiece type, workpiece number, and workpiece transport image; Based on the workpiece transport image, generate and execute feature extraction instructions. The feature extraction instructions are used to extract the workpiece convex point features in the workpiece transport image. The workpiece convex point features are the three-dimensional positions of each vertex of the workpiece. The system acquires the protrusion features of the workpiece; Based on the protrusion features of the workpiece, generate and execute adjacent vertex connection instructions. The adjacent vertex connection instructions are used to connect each adjacent vertex in three-dimensional space. The three-dimensional model after connecting adjacent vertices is the workpiece structural framework. Obtain the workpiece structural frame; Based on the workpiece transport image, generate and execute the structural surface filling command. The structural surface filling command is used to fill the workpiece structural surface into the workpiece structural frame according to the actual workpiece scale, forming a three-dimensional workpiece model. Obtain a 3D workpiece model; Based on the 3D workpiece model and workpiece number, a workpiece model marking and storage instruction is generated and executed. The workpiece model marking and storage instruction is used to mark and store the 3D workpiece model according to the workpiece number.

[0006] By adopting the above technical solution, the system automatically acquires the workpiece type, workpiece number, and workpiece conveying image. The system identifies the workpiece conveying image, executes feature extraction instructions, extracts the workpiece convex point features within the workpiece conveying image, and forms a three-dimensional positional relationship based on each vertex of the workpiece within the system. Then, it executes the connection of adjacent vertices, connecting each adjacent vertex in the three-dimensional space within the system to form a three-dimensional model workpiece structural framework. Next, it performs a structural surface filling operation, filling the workpiece structural surfaces into the workpiece structural framework according to the actual workpiece proportion, forming a three-dimensional workpiece model. Finally, it performs a workpiece model marking and storage operation, marking and storing the three-dimensional workpiece model according to the workpiece number. Thus, workers can identify the three-dimensional workpiece model to determine whether the workpiece is qualified, thereby helping to reduce the error rate of acceptance and improve the accuracy of workpiece acceptance.

[0007] Optionally, before generating structural surface filling instructions based on the workpiece transport image and performing the steps, the following steps are included: Get the number of identified structural surfaces; Retrieve the number of preset structural surfaces corresponding to the workpiece type from the preset database; Compare the number of identified structural surfaces with the number of preset structural surfaces for comparison; If the number of preset structural surfaces to be compared is greater than the number of already identified structural surfaces, a recognition failure prompt instruction will be generated. The recognition failure prompt instruction is used to inform the staff that the workpiece corresponding to the workpiece number has failed to be identified.

[0008] By adopting the above technical solution, the system automatically obtains the number of identified structural surfaces and retrieves the number of preset structural surfaces corresponding to the workpiece type from the preset database entered by the staff. The system automatically compares the number of identified structural surfaces with the number of preset structural surfaces. If the number of preset structural surfaces is greater than the number of identified structural surfaces, the system will execute the operation of recognition failure prompt, indicating to the staff that the workpiece corresponding to the workpiece number has failed to be recognized. This makes it easier for the staff to select the workpieces that failed to be recognized, reducing the phenomenon of workpieces being missed during acceptance.

[0009] Optionally, after comparing the number of identified structural surfaces with the preset number of structural surfaces for comparison, the following steps are included: If the number of identified structural surfaces is greater than the preset number of structural surfaces to be compared, an unidentified structural surface extraction instruction is generated and executed. The unidentified structural surface extraction instruction is used to extract the unidentified structural surfaces. Obtain unidentified structural surfaces; Based on the unrecognized structural surfaces, generate and execute the unrecognized structural surface highlighting command. The unrecognized structural surface highlighting command is used to set the highlighting of the unrecognized structural surfaces.

[0010] By adopting the above technical solution, if the number of identified structural surfaces is greater than the preset number of structural surfaces to be compared, the operation of extracting unidentified structural surfaces is performed to extract the unidentified structural surfaces. Then, the operation of highlighting the unidentified structural surfaces is performed to set the highlighting of the unidentified structural surfaces, thereby making it easier for staff to understand the location of the unidentified structural surfaces and to conduct actual acceptance of the workpieces corresponding to the unidentified structural surfaces.

[0011] Optionally, after the step of obtaining workpiece information, the following steps may be included: Obtain image noise values; Retrieve preset noise contrast values ​​from a preset database; Compare the preset noise contrast value with the image noise value; If the preset noise comparison value is greater than the image noise value, an image noise normal prompt instruction is generated and executed. The image noise normal prompt instruction is used to prompt the staff that the image noise is normal. Based on the image noise normal prompt instruction, an image collection instruction is generated and executed. The image collection instruction is used to collect and store images with normal noise.

[0012] By adopting the above technical solution, the system automatically obtains the image noise value and retrieves the preset noise comparison value input by the staff from the preset database. It compares the preset noise comparison value with the image noise value. If the preset noise comparison value is greater than the image noise value, it performs the operation of image noise normal prompting, prompting the staff that the image noise is normal. Then, it performs the image collection operation to collect and store the images with normal noise, thereby facilitating the screening of images with abnormal noise.

[0013] Optionally, after the step of comparing the preset noise contrast value and the image noise value, the following steps are included: If the preset noise contrast value is less than the image noise value, an image noise anomaly prompt instruction is generated and executed. The image noise anomaly prompt instruction is used to prompt staff that the image noise is abnormal. Based on the image noise anomaly warning instruction, a super-resolution reconstruction instruction is generated and executed. The super-resolution reconstruction instruction is used to fill in pixel values ​​to increase the image resolution.

[0014] By adopting the above technical solution, if the preset noise contrast value is less than the image noise value, an image noise abnormality prompt is executed to alert the staff that the image noise is abnormal. Then, a super-resolution reconstruction operation is performed to fill in the pixel values ​​to increase the image resolution, which helps to reduce image noise.

[0015] Optionally, before generating feature extraction instructions and executing steps based on the workpiece transport image, the following steps are included: Get the number of workpieces; Retrieve the preset comparison quantity corresponding to the workpiece type from the preset database; Compare the preset comparison quantity with the number of workpieces; If the number of workpieces is greater than the preset comparison number, an image segmentation instruction is generated and executed. The image segmentation instruction is used to segment multiple workpieces within an image for transmission.

[0016] By adopting the above technical solution, the system obtains the number of workpieces based on the workpiece conveying image, and queries the preset comparison number entered by the staff from the preset database. The preset comparison number is compared with the number of workpieces. If the number of workpieces is greater than the preset comparison number, the image segmentation operation is performed to segment the image so that each image contains only one workpiece.

[0017] Optionally, before the step of comparing the preset comparison quantity and the number of workpieces, the following steps are included: Obtain the distance between adjacent workpieces; Query the preset adjacent distance from the preset database; Compare the distance between adjacent workpieces with the preset adjacent distance; If the distance between adjacent workpieces is less than the preset adjacent distance, a proximity prompt instruction is generated and executed. The proximity prompt instruction is used to remind the staff that adjacent workpieces are close and the adjacent position cannot be identified.

[0018] By adopting the above technical solution, the system identifies the distance between adjacent workpieces in the image, queries the preset adjacent distance entered by the staff from the preset database, compares the distance between adjacent workpieces with the preset adjacent distance, and if the distance between adjacent workpieces is less than the preset adjacent distance, it performs a proximity prompt operation to prompt the staff that the adjacent workpieces are close and the actual structural surface corresponding to the workpiece cannot be identified at the proximity position, so that the staff can adjust the distance between the workpieces in a timely manner.

[0019] Secondly, this application provides an image data processing system, which adopts the following technical solution: include: The information acquisition module is used to acquire workpiece type, workpiece number, and workpiece transport images; The feature extraction module is used to extract the convex features of the workpiece within the workpiece transport image. Vertex connection module, used to connect adjacent vertices in three-dimensional space; The structural surface filling module fills in the structural surfaces of the workpiece according to the actual workpiece proportions, forming a three-dimensional workpiece model.

[0020] Thirdly, this application provides a smart terminal, which adopts the following technical solution: A smart terminal, comprising: Memory is used to store computer programs that can run on a processor; The processor, when running the computer program, is capable of performing the steps of any of the methods described above.

[0021] By adopting the above technical solution, the memory can store information, the processor can retrieve the information and issue control instructions, ensuring the orderly execution of the program and achieving the effect of the above solution.

[0022] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution: A computer-readable storage medium storing a computer program that can be loaded by a processor and executed by any of the methods described above.

[0023] By adopting the above technical solution, when the computer-readable storage medium is loaded into any computer, any computer can execute the image data processing method provided in this application.

[0024] In summary, this application includes at least one of the following image data processing methods that offer beneficial technical effects: 1. The system automatically acquires the workpiece type, workpiece number, and workpiece conveying image. The system recognizes the workpiece conveying image, executes feature extraction instructions, extracts the workpiece convex point features within the image, establishes a three-dimensional positional relationship based on the vertices of the workpiece within the system, and then executes the connection of adjacent vertices. This connects adjacent vertices in the system's three-dimensional space to form a three-dimensional workpiece structural framework. Next, a structural surface filling operation is performed, filling the workpiece structural surfaces into the framework according to the actual workpiece proportions to form a three-dimensional workpiece model. Finally, a workpiece model marking and storage operation is performed, marking and storing the three-dimensional workpiece model according to the workpiece number. Workers can then identify the three-dimensional workpiece model to determine whether the workpiece is qualified, thereby reducing the error rate in acceptance and improving the accuracy of workpiece acceptance. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the overall process of an image data processing method.

[0026] Figure 2 This is a flowchart of the sub-steps in S106 of this application.

[0027] Figure 3 This is a supplementary flowchart of an image data processing method according to this application.

[0028] Figure 4 This is a supplementary flowchart of an image data processing method according to this application.

[0029] Figure 5 This is a flowchart of the sub-steps in S303 of this application.

[0030] Figure 6 This is a supplementary flowchart of an image data processing method according to this application.

[0031] Figure 7 This is a flowchart of the sub-steps in S403 of this application.

[0032] Figure 8 This is a block diagram of the modules implementing image data processing in an embodiment of this application.

[0033] Figure labeling: 1. Information acquisition module; 2. Feature extraction module; 3. Vertex connection module; 4. Structure surface filling module; 5. Structure surface recognition prompt module; 6. Noise and abnormal image screening module; 7. Image segmentation module. Detailed Implementation

[0034] The present application will be further described in detail below with reference to all the accompanying drawings.

[0035] This application discloses an image data processing method.

[0036] In one embodiment, refer to Figure 1 An image data processing method, specifically including the following steps: S101. Obtain workpiece information; Specifically, the system automatically acquires information such as workpiece type, workpiece number, and workpiece transport images through a camera system.

[0037] S102. Based on the workpiece transport image, generate feature extraction instructions and execute them; S103. The system acquires the protrusion features of the workpiece; S104. Generate and execute the adjacent vertex connection instruction based on the protrusion characteristics of the workpiece; S105. Obtain the workpiece structural frame; S106. Generate and execute structural surface filling instructions based on the workpiece transport image; S107. Obtain the three-dimensional workpiece model; S108. Generate and execute workpiece model mark storage instructions based on the 3D workpiece model and workpiece number.

[0038] Specifically, the system identifies the workpiece conveying image, executes feature extraction instructions, and extracts the convex features of the workpiece within the conveying image. Taking a regular dodecahedron as an example, the system establishes a three-dimensional positional relationship based on the vertices of the workpiece, then executes the connection of adjacent vertices, connecting each adjacent vertex in the three-dimensional space of the system to form a three-dimensional model workpiece structural framework, i.e., forming a regular dodecahedron structural framework. Next, the system performs a structural surface filling operation, filling the workpiece structural surfaces into the structural framework according to the actual workpiece proportions to form a three-dimensional workpiece model, i.e., forming a regular icosahedron. Then, the system performs a workpiece model marking and storage operation, marking and storing the three-dimensional workpiece model according to the workpiece number, using A1 and A2 as examples. Then, the staff can identify the three-dimensional workpiece model to determine whether the workpiece is qualified, and retrieve A1 and A2 for manual acceptance, which helps to reduce the error rate of acceptance and improve the accuracy of workpiece acceptance.

[0039] In one embodiment, refer to Figure 2 To reduce the occurrence of omissions in workpiece acceptance, the following steps are included before step S106: S1061. Obtain the number of identified structural surfaces; S1062. Obtain the number of preset structural surfaces corresponding to the workpiece type from the preset database; S1063. Compare the number of identified structural surfaces with the number of preset structural surfaces for comparison; S1064. If the number of preset structural surfaces to be compared is greater than the number of identified structural surfaces, a recognition failure prompt instruction will be generated.

[0040] Specifically, the system automatically obtains the number of identified structural surfaces (taking 10 as an example) and retrieves the number of preset structural surfaces corresponding to the workpiece type from the preset database entered by the staff (taking 11 as an example). The system automatically compares the number of identified structural surfaces with the number of preset structural surfaces. If the number of preset structural surfaces is greater than the number of identified structural surfaces, the system will execute an operation to prompt the staff that the workpiece corresponding to the workpiece number has failed to be identified. This makes it easier for the staff to select the workpieces that failed to be identified, reducing the phenomenon of workpieces being missed during acceptance.

[0041] In one embodiment, refer to Figure 3 To help staff identify the locations of unidentified structural surfaces, the following steps are included after step S1063: S201. If the number of identified structural surfaces is greater than the preset number of structural surfaces to be compared, then generate and execute the command to extract unidentified structural surfaces. S202, Obtain unidentified structural surfaces; S203. Generate and execute the unrecognized structure surface highlighting command based on the unrecognized structure surface.

[0042] Specifically, if the number of identified structural surfaces is 11 and the preset number of structural surfaces for comparison is 10, and the number of identified structural surfaces is greater than the preset number of structural surfaces for comparison, the operation of extracting unidentified structural surfaces is performed to extract the unidentified structural surfaces. Then, the operation of highlighting unidentified structural surfaces is performed to highlight the unidentified structural surfaces, thereby making it easier for staff to understand the location of unidentified structural surfaces and to manually inspect the workpieces corresponding to the unidentified structural surfaces.

[0043] In one embodiment, refer to Figure 4 To facilitate the filtering of noisy and abnormal images, the following steps are included after step S101: S301. Obtain image noise values; S302. Obtain a preset noise comparison value from a preset database; S303. Compare the preset noise comparison value with the image noise value; S304. If the preset noise comparison value is greater than the image noise value, generate and execute the image noise normal prompt command. S305. Based on the image noise normal prompt instruction, generate an image collection instruction and execute it.

[0044] Specifically, the system automatically obtains the image noise value and retrieves the preset noise comparison value input by the staff from the preset database. It compares the preset noise comparison value with the image noise value. If the preset noise comparison value is greater than the image noise value, it performs an image noise normal prompt operation, prompting the staff that the image noise is normal. Then, it performs an image aggregation operation to collect and store images with normal noise, thereby facilitating the filtering out of images with abnormal noise.

[0045] In one embodiment, refer to Figure 5 To reduce image noise, the following steps are included after step S303: S3031. If the preset noise contrast value is less than the image noise value, generate and execute an image noise anomaly prompt command. S3032. Based on the image noise anomaly prompt instruction, generate and execute the super-resolution reconstruction instruction.

[0046] Specifically, if the preset noise contrast value is less than the image noise value, an image noise anomaly warning is issued to the staff, and then a super-resolution reconstruction operation is performed to fill in pixel values ​​to increase the image resolution. First, the target resolution is determined. For example, if the original image is 100x100 pixels and it is increased to 200x200 pixels, the target resolution is 200x200 pixels. Then, a new image is created, that is, a blank image of 200x200 pixels is created. Then, the original image is copied, and then pixel values ​​are filled. For each blank pixel in the new image, the value of the adjacent pixel is used to fill it. Finally, the new image is saved, which helps to reduce image noise.

[0047] In one embodiment, refer to Figure 6 In order to segment the image and facilitate the extraction of workpiece convex features, the following steps are included before step S102: S401. Obtain the number of workpieces; S402. Query the preset comparison quantity corresponding to the workpiece type from the preset database; S403, compare the preset comparison quantity and the number of workpieces; S404. If the number of workpieces is greater than the preset comparison number, an image segmentation instruction is generated and executed.

[0048] Specifically, the system obtains the number of workpieces based on the workpiece conveying image, and queries the preset comparison number entered by the staff from the preset database. It compares the preset comparison number with the number of workpieces. If the number of workpieces is greater than the preset comparison number, it performs image segmentation to divide the image into segments, with each image containing only one workpiece, which facilitates the extraction of the workpiece's convex features.

[0049] In one embodiment, refer to Figure 7 To facilitate timely adjustments to the workpiece distance by staff, the following steps are included after step S403: S4031. Obtain the distance between adjacent workpieces; S4032. Query the preset adjacent distance from the preset database; S4033. Compare the distance between adjacent workpieces with the preset adjacent distance; S4034. If the distance between adjacent workpieces is less than the preset adjacent distance, generate and execute the proximity prompt command.

[0050] Specifically, the system identifies the distance between adjacent workpieces in the image, queries the preset adjacent distance entered by the staff from the preset database, compares the distance between adjacent workpieces with the preset adjacent distance, and if the distance between adjacent workpieces is less than the preset adjacent distance, it performs a proximity prompt operation to prompt the staff that the adjacent workpieces are close and the actual structural surface corresponding to the workpiece cannot be identified at the proximity position, so that the staff can adjust the distance between the workpieces in a timely manner.

[0051] In one embodiment, refer to Figure 8 This application also discloses an image data processing system that can achieve the same technical effect as the image data processing method described above, specifically including the following modules: Information acquisition module 1 is used to acquire workpiece type, workpiece number, and workpiece transport image; Feature extraction module 2 is used to extract the protrusion features of the workpiece in the workpiece transport image; Vertex connection module 3 is used to connect adjacent vertices in three-dimensional space; Structural surface filling module 4 fills the structural surfaces of the workpiece according to the actual workpiece proportions to form a three-dimensional workpiece model. The structural surface recognition prompt module 5 is used to prompt the staff whether the workpiece structural surface has been successfully recognized. Noise-abnormal image filtering module 6 is used to filter out noise-abnormal images; Image segmentation module 7 is used to segment multiple workpiece images.

[0052] This application also discloses a smart terminal, including a memory and a processor. The memory stores a smart computer program. The processor, when running the smart computer program, is capable of executing the steps of the image data processing method described above. The smart computer program can use known processing procedures to perform a series of steps such as querying, comparing, and judging data, thereby realizing image data processing.

[0053] This application also discloses a computer-readable storage medium that stores a computer program that can be loaded by a processor and executed as described above in the image data processing method. The computer-readable storage medium includes, for example, various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0054] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. An image data processing method, characterized in that, include: Obtain workpiece information, including workpiece type, workpiece number, and workpiece transport image; Based on the workpiece transport image, generate and execute feature extraction instructions. The feature extraction instructions are used to extract the workpiece convex point features in the workpiece transport image. The workpiece convex point features are the three-dimensional positions of each vertex of the workpiece. The system acquires the protrusion features of the workpiece; Based on the protrusion features of the workpiece, generate and execute adjacent vertex connection instructions. The adjacent vertex connection instructions are used to connect each adjacent vertex in three-dimensional space. The three-dimensional model after connecting adjacent vertices is the workpiece structural framework. Obtain the workpiece structural frame; Based on the workpiece transport image, generate and execute the structural surface filling command. The structural surface filling command is used to fill the workpiece structural surface into the workpiece structural frame according to the actual workpiece scale, forming a three-dimensional workpiece model. Obtain a 3D workpiece model; Based on the 3D workpiece model and workpiece number, generate and execute the workpiece model marking and storage instruction. The workpiece model marking and storage instruction is used to mark and store the 3D workpiece model according to the workpiece number. Before generating structural surface filling instructions based on the workpiece transport image and executing the steps, the process also includes: Get the number of identified structural surfaces; Retrieve the number of preset structural surfaces corresponding to the workpiece type from the preset database; Compare the number of identified structural surfaces with the number of preset structural surfaces for comparison; If the number of preset structural surfaces to be compared is greater than the number of structural surfaces that have been identified, a recognition failure prompt instruction will be generated. The recognition failure prompt instruction is used to inform the staff that the workpiece corresponding to the workpiece number has failed to be identified. After comparing the number of identified structural surfaces with the preset number of structural surfaces, the process also includes: If the number of identified structural surfaces is greater than the preset number of structural surfaces to be compared, an unidentified structural surface extraction instruction is generated and executed. The unidentified structural surface extraction instruction is used to extract the unidentified structural surfaces. Obtain unidentified structural surfaces; Based on the unrecognized structural surfaces, generate and execute the unrecognized structural surface highlighting command. The unrecognized structural surface highlighting command is used to set the highlighting of the unrecognized structural surfaces.

2. The image data processing method according to claim 1, characterized in that, After the step of obtaining workpiece information, the following steps are included: Obtain image noise values; Retrieve preset noise contrast values ​​from a preset database; Compare the preset noise contrast value with the image noise value; If the preset noise comparison value is greater than the image noise value, an image noise normal prompt instruction is generated and executed. The image noise normal prompt instruction is used to prompt the staff that the image noise is normal. Based on the image noise normal prompt instruction, an image collection instruction is generated and executed. The image collection instruction is used to collect and store images with normal noise.

3. The image data processing method according to claim 1, characterized in that, After comparing the preset noise comparison value and the image noise value, the process includes: If the preset noise contrast value is less than the image noise value, an image noise anomaly prompt instruction is generated and executed. The image noise anomaly prompt instruction is used to prompt staff that the image noise is abnormal. Based on the image noise anomaly prompt, a super-resolution reconstruction instruction is generated and executed. The super-resolution reconstruction instruction is used to fill in pixel values ​​to increase the resolution of the image. The super-resolution reconstruction instructions include confirming the target resolution, creating a new image, copying the original image, filling in pixel values, and saving the new image.

4. The image data processing method according to claim 1, characterized in that, Before generating feature extraction instructions and executing steps based on the workpiece transport image, the process includes: Get the number of workpieces; Retrieve the preset comparison quantity corresponding to the workpiece type from the preset database; Compare the preset comparison quantity with the number of workpieces; If the number of workpieces is greater than the preset comparison number, an image segmentation instruction is generated and executed. The image segmentation instruction is used to segment multiple workpieces within an image for transmission.

5. The image data processing method according to claim 4, characterized in that, Before the step of comparing the preset comparison quantity and the number of workpieces, the following steps are included: Obtain the distance between adjacent workpieces; Query the preset adjacent distance from the preset database; Compare the distance between adjacent workpieces with the preset adjacent distance; If the distance between adjacent workpieces is less than the preset adjacent distance, a proximity prompt instruction is generated and executed. The proximity prompt instruction is used to remind the staff that adjacent workpieces are close and the adjacent position cannot be identified.

6. An image data processing system, comprising the method according to any one of claims 1-5, characterized in that, include: Information acquisition module (1) is used to acquire workpiece type, workpiece number and workpiece conveying image; Feature extraction module (2) is used to extract the protrusion features of the workpiece in the workpiece transport image; Vertex connection module (3) is used to connect adjacent vertices in three-dimensional space; The structural surface filling module (4) fills the structural surface of the workpiece according to the actual workpiece ratio to form a three-dimensional workpiece model.

7. A smart terminal, characterized in that, include: Memory is used to store computer programs that can run on a processor; The processor, when running the computer program, is capable of performing the steps of the method as described in any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that: The computer program is stored that can be loaded by a processor and executed according to any one of claims 1 to 5.

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