Model Disassembly Method, Device and System

Abstract

The present application discloses a model disassembly method, apparatus and system, relating to the technical field of mechanical design. The method includes: obtaining part data of a model; clustering the part data to divide the parts of the model into multiple groups; and disassembling the model according to the groups. The solution of the present application first groups the parts in the model, and then disassembles the model based on the grouping result, enabling designers or maintenance engineers to quickly understand the internal associations and structures of the model.

Description

Technical Field

[0001] This application relates to the field of mechanical design, and in particular, to a model disassembly method, device, and system. Background Art

[0002] Due to the variety of industrial equipment models and the significant differences in their internal structures, when editing a model, it is necessary to view and analyze its components in detail. Traditional model disassembly is based on the assembly relationships of each component of the model, which is too complex and not conducive to designers or maintenance engineers quickly understanding the model. Summary of the Invention

[0003] The purpose of this application is to provide a model disassembly method, device, and system, so as to solve the problem that the existing model disassembly method in the prior art is not convenient for relevant personnel to quickly understand the model.

[0004] In a first aspect, to achieve the above object, this application provides a model disassembly method, including:

[0005] Obtain the part data of the model;

[0006] Cluster the part data to divide the parts of the model into multiple groups;

[0007] Disassemble the model according to the groups.

[0008] Optionally, obtaining the part data of the model includes:

[0009] Based on the imported model data object, obtain the part list of the model, where the part list includes the position information and bounding box values of the parts;

[0010] According to the part list, obtain the point cloud data of each part.

[0011] Optionally, clustering the part data to divide the parts of the model into multiple groups includes:

[0012] Use the K-means clustering algorithm to cluster the point cloud data;

[0013] Based on the clustering result, divide the parts of the model into multiple groups.

[0014] Optionally, based on the clustering result, dividing the parts of the model into multiple groups includes:

[0015] Based on the clustering result, in the point cloud data of each part, determine the clustering category where the most point cloud data is located;

[0016] Determine the corresponding grouping of the parts according to the clustering category where the most point cloud data is located.

[0017] Optionally, the method further includes:

[0018] Receive the first input from the user;

[0019] In response to the first input, distinguish and display each group of parts according to a preset rule.

[0020] Optionally, distinguishing and displaying each group of parts according to a preset rule includes:

[0021] Adjust the material of each group of parts according to the first input;

[0022] Based on the corresponding relationship between the preset material and color, display each group of parts.

[0023] In a second aspect, an embodiment of the present application further provides a model disassembly system, including: a processor, a memory, and a program stored on the memory and executable on the processor, and when the program is executed by the processor, the steps of the model disassembly method described above are implemented.

[0024] In a third aspect, an embodiment of the present application further provides a model disassembly device, including:

[0025] An acquisition module, configured to acquire part data of a model;

[0026] A grouping module, configured to cluster the part data and divide the parts of the model into multiple groups;

[0027] A disassembly module, configured to disassemble the model according to the grouping.

[0028] In a fourth aspect, an embodiment of the present application further provides a readable storage medium, on which a program is stored, and when the program is executed by a processor, the steps of the model disassembly method described above are implemented.

[0029] The above technical solutions of the present application have at least the following beneficial effects:

[0030] For the model disassembly method in the embodiment of the present application, first, acquire part data of the model; second, cluster the part data and divide the parts of the model into multiple groups; finally, disassemble the model according to the grouping. In this way, there can be a certain connection between the parts in each group of parts. In this way, disassembling the model based on the grouping of the parts in the model is convenient for relevant personnel to quickly view and understand the internal association and structure between the model parts. Description of the Drawings

[0031] Figure 1One of the flow diagrams of the model disassembly method according to an embodiment of the present application;

[0032] Figure 2 One of the flow diagrams of the part grouping according to an embodiment of the present application;

[0033] Figure 3 The structural diagram of the model disassembly device according to an embodiment of the present application;

[0034] Figure 4 The structural diagram of the model disassembly system according to an embodiment of the present application. Detailed implementation manners

[0035] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present application without creative efforts shall fall within the protection scope of the present application.

[0036] The terms "first", "second", etc. in the specification and claims of the present application are used to distinguish similar objects, rather than to describe a specific order or sequence. It should be understood that such data may be interchanged under appropriate circumstances so that the embodiments of the present application can be implemented in an order other than those illustrated or described herein. In addition, "and / or" in the specification and claims means at least one of the connected objects, and the character " / " generally indicates an "or" relationship between the associated objects before and after.

[0037] Before describing the embodiments of the present application, the related technologies will be described first:

[0038] Point cloud data: A set of vectors in a three-dimensional coordinate system. In addition to having geometric positions, some point cloud data also has color information. The color information is usually obtained by a camera to acquire a color image, and then the color of the pixel at the corresponding position is assigned to the corresponding point in the point cloud.

[0039] Next, the model disassembly method, device, and system provided by the embodiments of the present application will be described in detail in conjunction with the accompanying drawings, through specific embodiments and their application scenarios.

[0040] As Figure 1 shown, it is one of the flow diagrams of the model disassembly method according to an embodiment of the present application, and the method includes:

[0041] Step 101: Obtain the part data of the model;

[0042] Here, it should be noted that the execution subject of the embodiments of the present application can be a 3D creation engine, a 3D development tool, or 3D modeling software, such as unity, etc. This step specifically involves obtaining the part data in the model imported into the execution subject (unity); specifically, it can include: importing the model into unity and initializing it through a script to obtain the part data in the model.

[0043] Step 102: Cluster the part data and divide the parts of the model into multiple groups;

[0044] In this step, when clustering the part data, the structural characteristics of the parts are fully considered, avoiding the influence on the clustering effect when the number of parts is small.

[0045] Step 103: Disassemble the model according to the groups.

[0046] In this step, during the specific disassembly, different-dimensional disassembly can be performed for each type of part. In this way, on the one hand, it is conducive for relevant personnel to view the internal associations and structures between the model parts in more detail; on the other hand, it reduces the complexity of part disassembly and makes the part disassembly targeted.

[0047] The model disassembly method of the embodiments of the present application first obtains the part data of the model; secondly, clusters the part data and divides the parts of the model into multiple groups; finally, disassembles the model according to the groups. In this way, there can be a certain mutual connection between the parts in each group of parts. In this way, disassembling the model based on the grouping of the parts in the model facilitates relevant personnel to quickly view and understand the internal associations and structures between the model parts.

[0048] As an optional implementation manner, step 101, obtaining the part data of the model, includes:

[0049] Based on the imported model data object, obtain the part list of the model, where the part list includes the position information and bounding box values of the parts;

[0050] In this step, the model data object can be obtained by initializing the imported 3D model through a script. The position information of the part is the three-dimensional position information of the part, that is, the three-dimensional coordinates of the part. The part list can be a list of part objects in unity.

[0051] According to the part list, obtain the point cloud data of each part.

[0052] Here, it should be noted that Mesh is a component in Unity, called the mesh component. Mesh refers to the mesh of the model. A 3D model is composed of polygons stitched together, and a complex polygon is actually composed of multiple triangular faces stitched together. Therefore, the surface of a 3D model is composed of multiple interconnected triangular faces. In three-dimensional space, the set of points that make up these triangular faces and the edges of the triangles is Mesh. Among them, the Mesh set contains the set of triangular face vertices, namely the Vertices array, and the array contains the position, tangent, and normal information of the vertices. Among them, the Vertices array is the point cloud data of the part.

[0053] As a specific implementation, cluster the part data, and divide the parts of the model into multiple groups, including:

[0054] Use the K-means clustering algorithm to cluster the point cloud data;

[0055] Here, it should be noted that the K-means clustering algorithm: is an iterative clustering analysis algorithm. Its steps are to pre-divide the data into K groups, then randomly select K objects as the initial clustering centers, and then calculate the distance between each object and each seed clustering center, and assign each object to the clustering center closest to it. The clustering centers and the objects assigned to them represent a cluster. Each time a sample is assigned, the clustering center of the cluster will be recalculated based on the existing objects in the cluster. This process will repeat continuously until a certain termination condition is met. The termination condition can be that no (or the minimum number) of objects are reassigned to different clusters, no (or the minimum number) of clustering centers change anymore, and the sum of squared errors is locally minimized.

[0056] Based on the clustering result, divide the parts of the model into multiple groups.

[0057] This optional implementation groups the parts based on the classification of the point cloud data, avoiding the problem of inaccurate display positions after disassembly due to the offset of the center point positions of some parts in the model, reducing the complexity of part disassembly and making the part disassembly targeted, and can also constrain the behavior of the model.

[0058] Here, it should also be noted that this specific implementation can be implemented based on the following code:

[0059]

[0060] As a more specific implementation, based on the clustering result, divide the parts of the model into multiple groups, including:

[0061] Based on the clustering result, in the point cloud data of each part, determine the clustering category where the point cloud data with the largest quantity is located;

[0062] This step can specifically be: Based on the clustering result, determine the number of point cloud data of each part in each type of clustering. By comparing the numbers of point cloud data of a part in each clustering, determine the clustering category with the largest number of point cloud data.

[0063] Determine the grouping of the corresponding part according to the clustering category where the point cloud data with the largest number is located.

[0064] That is to say, in this specific implementation manner, according to the clustering result of the point cloud data, a vote is conducted on the parts to classify the parts into the clustering category where the largest number of point cloud data is located.

[0065] For example: A 3D model has 100 components. One component has 100 vertices, and there are a total of 10,000 vertex coordinate values. After clustering, 4 types are obtained, and the 10,000 vertices are divided into 4 types. Voting means that for each of the 100 vertices of each component, if the number of vertices of a certain type is the largest, then the component is classified into that type (for example, among the 100 vertices of this component, there are 20 vertices of type A, 25 vertices of type B, 40 vertices of type C, and 15 vertices of type D, then this component belongs to type C); and so on, classify all the components to obtain the type set where each component is located.

[0066] As an optional implementation manner, the method further includes:

[0067] Receive the first input from the user;

[0068] In response to the first input, distinguish and display each group of parts according to a preset rule.

[0069] Specifically, distinguishing and displaying each group of parts according to a preset rule includes:

[0070] Adjust the material of each group of parts according to the first input;

[0071] Based on the preset correspondence between the material and the color, display each group of parts.

[0072] That is to say, after classifying the point cloud data of the model, the user can perform the first input to the model disassembly system, so that it adjusts the material of each group of parts based on this first input. Among them, different materials correspond to different display colors, so that the colors of each group of parts of the displayed model are different, so as to facilitate relevant personnel to quickly distinguish different groups of parts.

[0073] Next, in combination with Figure 2 , the process of part grouping in the model disassembly method of the embodiment of the present application will be described:

[0074] Step 201, initialize data to obtain model data; for example, import a 3D model into unity and initialize it through a script to obtain a basic model data object;

[0075] Step 202, obtain part data and create a part list; for example, obtain the x, y, z coordinates and bounds values of the position of each component and save them into a list of part objects;

[0076] Step 203, obtain part point cloud data; that is, based on the part data in step 202, obtain the vertices point cloud data of each point;

[0077] Step 204, cluster the part point cloud data;

[0078] Step 205, vote on the parts according to the clustering result of the part point cloud data and classify them into the class where the most points are located; that is, after classifying the point cloud data, compare them one by one and calculate the class with the most point cloud data, which is the class of the key parts (components) of the model;

[0079] Step 206: Group according to the part classification result to obtain the model part grouping result.

[0080] In addition, after step 206, the method further includes: after classifying the point cloud data of the model, obtaining the actual model grouping result and distinguishing each group of parts by changing the material of the model.

[0081] For the model disassembly method of the embodiment of the present application, first, obtain the part data of the model; secondly, cluster the part data to divide the parts of the model into multiple groups; finally, disassemble the model according to the groups. In this way, there can be a certain mutual connection between the parts in each group of parts. In this way, disassembling the model based on the grouping of the parts in the model is convenient for relevant personnel to quickly view and understand the internal association and structure between the model parts.

[0082] As Figure 3 shown, the embodiment of the present application also provides a model disassembly device, including:

[0083] An acquisition module 301, configured to acquire part data of a model;

[0084] A grouping module 302, configured to cluster the part data to divide the parts of the model into multiple groups;

[0085] A disassembly module 303, configured to disassemble the model according to the groups.

[0086] The model disassembly device according to the embodiments of the present application, first, the acquisition module 301 acquires the part data of the model; secondly, the grouping module 302 clusters the part data, and divides the parts of the model into multiple groups; finally, the disassembly module 303 disassembles the model according to the groups. In this way, there can be a certain interconnection between the parts in each group of parts. In this way, disassembling the model based on the grouping of the parts in the model facilitates relevant personnel to quickly view and understand the internal association and structure between the model parts.

[0087] Optionally, the acquisition module 301 includes:

[0088] The first acquisition sub-module is used to acquire the part list of the model based on the imported model data object, where the part list includes the position information and bounding box value of the part;

[0089] The second acquisition sub-module is used to acquire the point cloud data of each part according to the part list.

[0090] Optionally, the disassembly module 303 includes:

[0091] The clustering sub-module is used to cluster the point cloud data by using the K-means clustering algorithm;

[0092] The grouping sub-module is used to divide the parts of the model into multiple groups based on the clustering result.

[0093] Optionally, the grouping sub-module includes:

[0094] The first determination unit is used to determine the clustering category where the most point cloud data is located in the point cloud data of each part based on the clustering result;

[0095] The second determination unit is used to determine the grouping of the corresponding parts according to the clustering category where the most point cloud data is located.

[0096] Optionally, the device further includes:

[0097] The receiving module is used to receive the first input of the user;

[0098] The processing module is used to distinguish and display each group of parts according to a preset rule in response to the first input.

[0099] Optionally, the processing module includes:

[0100] The adjustment sub-module is used to adjust the material of each group of parts according to the first input;

[0101] The display sub-module is used to display each group of parts based on the corresponding relationship between the preset material and color.

[0102] As Figure 4 shown, an embodiment of the present application further provides a model disassembling system, including: a processor 400, a memory 420, and a program stored on the memory 420 and executable on the processor 400. When the program is executed by the processor 400, it implements each process of the model disassembling method embodiment described above and can achieve the same technical effects. To avoid repetition, it will not be described in detail here.

[0103] The transceiver 410 is used to receive and send data under the control of the processor 400.

[0104] Among them, in Figure 4 , the bus architecture may include any number of interconnected buses and bridges, specifically various circuits of one or more processors represented by the processor 400 and the memory represented by the memory 420 are linked together. The bus architecture can also link together various other circuits such as peripheral devices, voltage regulators, and power management circuits, which are well known in the art. Therefore, they will not be further described herein. The bus interface provides an interface. The transceiver 410 may be multiple components, that is, including a transmitter and a receiver, and provides a unit for communicating with various other devices on the transmission medium. For different user devices, the user interface 430 may also be an interface capable of externally connecting or internally connecting required devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, etc.

[0105] The processor 400 is responsible for managing the bus architecture and general processing, and the memory 420 may store data used by the processor 400 when performing operations.

[0106] In addition, an embodiment of the present application further provides a readable storage medium. A program is stored on the readable storage medium. When the program is executed by the processor, it implements each process of the model disassembling method embodiment described above and can achieve the same technical effects. To avoid repetition, it will not be described in detail here. Among them, the readable storage medium is, for example, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disc, etc.

[0107] Finally, it should also be noted that in this text, relational terms such as first and second are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device comprising a series of elements not only includes those elements, but also includes other elements not expressly listed, or further includes elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0108] The above is the preferred embodiment of the present application. It should be pointed out that for those of ordinary skill in the art, without departing from the principle described in the present application, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present application.

Claims

1. A model disassembly method, characterized in that, it includes: Obtain the part data of the model; wherein, the part data is point cloud data composed of a Vertices array, and the Vertices array includes the vertices of each triangular face that makes up the part; Cluster the part data, vote on the parts according to the clustering results, and divide the parts of the model into multiple groups. Among them, the group where the part is located is the clustering category where the most point cloud data of the part is located; Disassemble the model according to the grouping; Among them, disassembling the model according to the grouping includes: Disassemble each group of parts in different dimensions.

2. The method according to claim 1, characterized in that, obtaining the part data of the model includes: Based on the imported model data object, obtain the part list of the model, wherein the part list includes the position information and bounding box value of the part; According to the part list, obtain the point cloud data of each part.

3. The method according to claim 2, characterized in that, clustering the part data and dividing the parts of the model into multiple groups includes: Use the K-means clustering algorithm to cluster the point cloud data; Based on the clustering results, divide the parts of the model into multiple groups.

4. The method according to claim 3, characterized in that, based on the clustering results, dividing the parts of the model into multiple groups includes: Based on the clustering results, in the point cloud data of each part, determine the clustering category where the most point cloud data is located; According to the clustering category where the most point cloud data is located, determine the grouping of the corresponding part.

5. The method according to claim 1, characterized in that, the method further includes: Receive the first input from the user; In response to the first input, distinguish and display each group of parts according to preset rules.

6. The method according to claim 5, characterized in that, distinguishing and displaying each group of parts according to preset rules includes: According to the first input, adjust the material of each group of parts; Based on the preset correspondence between material and color, display each group of parts.

7. A model disassembly system, characterized in that, it includes: A processor, a memory, and a program stored on the memory and executable on the processor. When the program is executed by the processor, it implements the steps of the model disassembly method according to any one of claims 1 to 6.

8. A model disassembly device, characterized in that, it includes: An acquisition module for acquiring the part data of the model; wherein, the part data is point cloud data composed of a Vertices array, and the Vertices array includes the vertices of each triangular face that makes up the part; A grouping module for clustering the part data, voting on the parts according to the clustering results, and dividing the parts of the model into multiple groups. Among them, the group where the part is located is the clustering category where the most point cloud data of the part is located; A disassembly module for disassembling the model according to the grouping; Among them, the disassembly module is specifically configured to: disassemble each group of parts in different dimensions.

9. A readable storage medium, characterized in that, a program is stored on the readable storage medium, and when the program is executed by a processor, the steps of the model disassembly method described in any one of claims 1 to 6 are implemented.

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