CN117236020A - Knowledge graph-based assembly simulation planning method, system, medium and equipment - Google Patents
Knowledge graph-based assembly simulation planning method, system, medium and equipment Download PDFInfo
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Abstract
The application discloses an assembly simulation planning method, a system, a medium and equipment based on a knowledge graph, wherein the method comprises the following steps: acquiring a loading part model and constructing an assembly simulation knowledge graph; constructing a three-dimensional assembly simulation scene according to the loading part model and the assembly simulation knowledge graph; constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm; calculating a collision avoidance assembly path of the collision bounding box based on an A-Star algorithm, and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path; the assembly process planning simulation can be realized, the assembly site is guided in a visual mode, and the precision and the efficiency of the assembly process simulation are improved.
Description
Technical Field
The application relates to the technical field of three-dimensional assembly simulation, in particular to an assembly simulation planning method, system, medium and equipment based on a knowledge graph.
Background
The assembly is an important link of the product manufacturing production process, is also a key step for determining product quality and performance indexes, and along with the development of the times, the manufacturing production industry is also turned towards a small-batch and customized mode, and the traditional two-dimensional paper process guiding assembly and the production field verification process scheme cannot rapidly cope with complex and various product assembly requirements, so that a three-dimensional assembly process planning method is required to be applied to realize visual guiding on field assembly and rapid reconstruction and verification of the assembly process scheme.
The knowledge graph is a semantic network for revealing the relationship between entities, and can intuitively express the relationship through a visual means. The knowledge required by the traditional assembly simulation planning method is usually stored in two-dimensional assembly technical specifications and process manuals, three-dimensional assembly elements such as assembly model files and the like are stored independently, process designers are required to manually classify and memorize the information, meanwhile, the assembly simulation platform is not directly connected with the assembly simulation knowledge, the process designers are required to understand the assembly simulation knowledge to a certain extent, and therefore, a great deal of time is required for completing one-time assembly simulation planning and the proficiency of the process designers is required.
The prior knowledge graph related technology research aiming at the assembly process field mainly comprises the processes of acquiring related assembly process information and constructing the knowledge graph corresponding to the existing assembly process, but has few assembly simulation flow, combines the real assembly process with the simulation process, and provides auxiliary knowledge graph related research for assembly process planning.
Disclosure of Invention
The application provides an assembly simulation planning method, system, medium and equipment based on a knowledge graph, which can realize assembly process planning simulation, guide an assembly site in a visual mode and improve the precision and efficiency of assembly process simulation.
In a first aspect, a knowledge graph-based assembly simulation planning method is provided, including the following steps:
acquiring a loading part model and constructing an assembly simulation knowledge graph;
constructing a three-dimensional assembly simulation scene according to the loading part model and the assembly simulation knowledge graph;
constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm;
and calculating a collision avoidance assembly path of the collision bounding box based on an A-Star algorithm, and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path.
According to the first aspect, in a first possible implementation manner of the first aspect, the step of constructing the assembly simulation knowledge graph specifically includes the following steps:
acquiring assembly process knowledge data, converting the assembly process knowledge data into natural language, converting the natural language into a csv format file through a knowledge extraction technology, and storing knowledge extraction results in a Neo4j graph database to obtain an assembly simulation knowledge graph.
According to the first aspect, in a second possible implementation manner of the first aspect, the step of constructing a three-dimensional assembly simulation scene according to the loader model and the assembly simulation knowledge graph specifically includes the following steps:
and acquiring actual assembly scene information of the loading part according to the loading part model, and mapping the actual assembly scene information into a three-dimensional assembly simulation scene through the assembly simulation knowledge graph.
According to the first aspect, in a third possible implementation manner of the first aspect, the step of constructing the collision bounding box of the loader model in the three-dimensional assembly simulation scene based on the AABB bounding box algorithm specifically includes the following steps:
and taking the central point of the loading part model as a base point, obtaining the maximum value and the minimum value of the loading part model in the three axial directions of the three-dimensional assembly simulation scene coordinate system x, y and z, determining eight vertex coordinates of a collision bounding box of the loading part model according to the maximum value and the minimum value in each direction, and constructing the collision bounding box of the loading part model according to the eight vertex coordinates of the collision bounding box.
In a third possible implementation manner of the first aspect, the step of calculating the collision avoidance assembly path of the collision bounding box based on the a-Star algorithm specifically includes the following steps:
step one, determining a starting node and a target node of a collision avoidance assembly path of the collision bounding box, and calculating cost estimation values of all collision-free neighborhood nodes of the starting node;
searching a collision-free neighborhood node corresponding to the minimum cost estimation value as a current node, and calculating cost estimation values of all collision-free neighborhood nodes of the current node;
step three, repeating the step two until the collision-free neighborhood node corresponding to the minimum cost estimation value is the target node, and ending the new current point search;
and step four, obtaining a collision avoidance assembly path of the collision bounding box according to the initial node, the target node and all the searched current nodes.
In a fourth possible implementation manner of the first aspect, after the step of calculating a collision avoidance assembly path of the collision avoidance bounding box based on the a-Star algorithm and assembling the collision avoidance bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path, the method specifically includes the following steps:
acquiring an assembly change demand, and detecting whether assembly constraints exist in the assembly simulation knowledge graph according to the assembly change demand;
if strong assembly constraints exist, suspending assembly changes; if weak assembly constraint exists, executing assembly change and prompting risk change; if no assembly constraints exist, an assembly change is performed.
In a second aspect, there is provided a knowledge-graph-based assembly simulation planning system, including:
the acquisition module is used for acquiring the loading part model and constructing an assembly simulation knowledge graph;
the simulation scene construction module is in communication connection with the acquisition module and is used for constructing a three-dimensional assembly simulation scene according to the assembly simulation knowledge graph and the assembly simulation model;
the packaging box construction module is in communication connection with the simulation scene construction module and is used for constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm; the method comprises the steps of,
the path planning module is in communication connection with the packaging box construction module and is used for calculating a collision prevention assembly path of the collision bounding box based on an A-Star algorithm and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision prevention assembly path.
In some embodiments, the system further includes an assembly modification module communicatively connected to the path planning module, configured to obtain an assembly modification requirement, and detect whether an assembly constraint exists in the assembly simulation knowledge graph according to the assembly modification requirement; if strong assembly constraints exist, suspending assembly changes; if weak assembly constraint exists, executing assembly change and prompting risk change; if no assembly constraints exist, an assembly change is performed.
In a third aspect, a storage medium is provided, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements a knowledge-graph based assembly simulation planning method as described above.
In a fourth aspect, an electronic device is provided, which includes a storage medium, a processor, and a computer program stored in the storage medium and capable of running on the processor, where the processor implements the knowledge-graph-based assembly simulation planning method as described above when running the computer program.
Compared with the prior art, the application has the following advantages: driving a three-dimensional assembly simulation scene to be constructed by constructing an assembly simulation knowledge graph, and constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm; and calculating a collision avoidance assembly path of the collision bounding box based on an A-Star algorithm, and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path. Therefore, the assembly process planning simulation can be realized, the assembly site is guided in a visual mode, and the accuracy and the efficiency of the assembly process simulation are improved.
Drawings
FIG. 1 is a schematic flow chart of an embodiment of an assembly simulation planning method based on a knowledge graph of the present application;
FIG. 2 is a flow chart of yet another embodiment of a knowledge-based assembly simulation planning method of the present application;
FIG. 3 is a flow chart of yet another embodiment of a knowledge-based assembly simulation planning method of the present application;
FIG. 4 is a flow chart of yet another embodiment of a knowledge-based assembly simulation planning method of the present application;
FIG. 5 is a flow chart of yet another embodiment of a knowledge-based assembly simulation planning method of the present application;
FIG. 6 is a flow chart of yet another embodiment of a knowledge-based assembly simulation planning method of the present application;
fig. 7 is a schematic structural diagram of an assembly simulation planning system based on a knowledge graph of the present application.
Detailed Description
Reference will now be made in detail to the present embodiments of the application, examples of which are illustrated in the accompanying drawings. While the application will be described in conjunction with the specific embodiments, it will be understood that they are not intended to limit the application to the described embodiments. On the contrary, it is intended to cover alternatives, modifications, and equivalents as may be included within the spirit and scope of the application as defined by the appended claims. It should be noted that the method steps described herein may be implemented by any functional block or arrangement of functions, and any functional block or arrangement of functions may be implemented as a physical entity or a logical entity, or a combination of both.
The present application will be described in further detail below with reference to the drawings and detailed description for the purpose of enabling those skilled in the art to understand the application better.
Note that: the examples to be described below are only one specific example, and not as limiting the embodiments of the present application necessarily to the following specific steps, values, conditions, data, sequences, etc. Those skilled in the art can, upon reading the present specification, make and use the concepts of the application to construct further embodiments not mentioned in the specification.
Referring to fig. 1, an embodiment of the present application provides an assembly simulation planning method based on a knowledge graph, including the following steps:
s100, acquiring a loading part model, and constructing an assembly simulation knowledge graph;
s200, constructing a three-dimensional assembly simulation scene according to the loading part model and the assembly simulation knowledge graph;
s300, constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm;
s400, calculating a collision avoidance assembly path of the collision bounding box based on an A-Star algorithm, and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path.
Specifically, in this embodiment, by constructing an assembly simulation knowledge graph, driving a three-dimensional assembly simulation scene to be constructed, and constructing a collision bounding box of the loader model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm; and calculating a collision avoidance assembly path of the collision bounding box based on an A-Star algorithm, and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path. Therefore, the assembly process planning simulation can be realized, the assembly site is guided in a visual mode, and the accuracy and the efficiency of the assembly process simulation are improved.
Preferably, in another embodiment of the present application, the step of constructing the assembly simulation knowledge graph includes the following steps:
acquiring assembly process knowledge data, converting the assembly process knowledge data into natural language, converting the natural language into a csv format file through a knowledge extraction technology, and storing knowledge extraction results in a Neo4j graph database to obtain an assembly simulation knowledge graph.
Specifically, in this embodiment, referring to fig. 2, the assembly process knowledge is obtained from different channels such as the assembly technical specification, the historical assembly data and the assembly process manual, wherein most of the knowledge is stored in the form of structured data, the assembly data are uniformly preprocessed into uniform natural language text, then the LTP language technology is adopted to analyze the processed natural language text corpus, the knowledge is extracted based on the rule template method, and finally the knowledge is converted into csv file format data by using script, so as to complete the extraction of the part of knowledge; for the knowledge of the assembly simulation elements and a small amount of knowledge of the non-formatted assembly process, the knowledge can be converted into a form of a table by a method defined by artificial statistics, and then the knowledge extraction is completed. And finally, storing and visually displaying the entity-entity relationship obtained through knowledge extraction based on the Neo4j platform to obtain the assembly simulation knowledge graph.
Preferably, in another embodiment of the present application, the step of constructing a three-dimensional assembly simulation scene according to the assembly simulation knowledge graph and the assembly model S200 specifically includes the following steps:
and acquiring actual assembly scene information of the loading part according to the loading part model, and mapping the actual assembly scene information into a three-dimensional assembly simulation scene through the assembly simulation knowledge graph.
Specifically, in this embodiment, referring to fig. 3, first, the scene information of the actual assembly that needs to be simulated, such as the position of the tool, the setting of the station, and the placement of the materials, needs to be measured and collected, and these information are also stored through the knowledge graph and correspond to the model in the assembly simulation space, so that the mapping process from the actual assembly scene information to the simulated assembly scene information is completed. And finally, the step of constructing the three-dimensional assembly simulation scene is completed by controlling the arrangement of the assembly scene model through the realization of the assembly simulation platform on the simulation assembly scene information obtained by mapping, and a foundation is laid for the subsequent assembly simulation links.
Preferably, in another embodiment of the present application, the step of constructing the collision bounding box of the loader model in the three-dimensional assembly simulation scene based on the AABB bounding box algorithm specifically includes the steps of:
taking the central point of the loading part model as a base point, obtaining the maximum value and the minimum value of the loading part model in three axial directions of a three-dimensional assembly simulation scene coordinate system x, y and z, determining eight vertex coordinates of a collision bounding box of the loading part model according to the maximum value and the minimum value in each direction, and constructing the collision bounding box of the loading part model according to the eight vertex coordinates of the collision bounding box
Referring to fig. 4, an AABB (Axis-aligned bounding box) package is formed by wrapping an object with a cuboid, and when a three-dimensional assembly simulation scene and an axisymmetric bounding box of a loading part are constructed, taking the center of the loading part model as a base point to obtain the maximum and minimum values of the loading part model in the x, y and z directions of the three-dimensional assembly simulation scene coordinate system, and quickly determining the space coordinates of eight vertexes of the model bounding box to determine the space range of the axisymmetric bounding box of the loading part model.
Preferably, in another embodiment of the present application, the step of calculating the collision avoidance assembly path of the collision bounding box based on the a-Star algorithm in S400 specifically includes the steps of:
step one, determining a starting node and a target node of a collision avoidance assembly path of the collision bounding box, and calculating cost estimation values of all collision-free neighborhood nodes of the starting node;
searching a collision-free neighborhood node corresponding to the minimum cost estimation value as a current node, and calculating cost estimation values of all collision-free neighborhood nodes of the current node;
step three, repeating the step two until the collision-free neighborhood node corresponding to the minimum cost estimation value is the target node, and ending the new current point search;
and step four, obtaining a collision avoidance assembly path of the collision bounding box according to the initial node, the target node and all the searched current nodes.
Specifically, in this embodiment, referring to fig. 5, the specific steps are as follows:
1) Determining a starting node s of a part model start And assembling the target node s end 。
2) Two lists of a candidate node list queue and a selected node list path are defined. The queue table mainly stores the calculated path cost estimated value f (n), but the neighborhood nodes of the queue table are not calculated; the path table mainly stores the searched and calculated nodes on the optimal path line;
3) Start node s start Put into path table, put its collision-free neighborhood node into queue table, point its parent pointer of neighborhood node to start node s start And calculating the cost estimation value of the neighborhood node, wherein the formula is as follows:
f(n)＝g(n)+h(n)；
where f (n) is a neighborhood node cost estimate (priority), g (n) is a node path dissipation value, h (n) is an expected distance between the neighborhood node and the target node send, and h (n) is often taken as the euclidean distance (Euclidean Distance) or manhattan distance (Manhattan Distance) from the current node to the target node. Each time the algorithm expands, the node with the smallest f (n) value is selected as the next node on the optimal path. Then the start node s start Putting the path table;
4) And selecting a node with the minimum cost estimation value from the queue table as a current node, putting the current node into the path table, searching a collision-free neighborhood node of which the current node does not belong to the path table, and calculating the cost estimation value of the neighborhood node. If the collision-free neighborhood nodes of the current node which do not belong to the path table are empty, jumping to the step (6); if the neighborhood node is not in the queue table, the parent pointer of the neighborhood node points to the current node and is put into the queue table; if the neighborhood node is in the queue table and the cost estimate of the neighborhood node calculated from the current node is less than the original cost estimate of the neighborhood node (with the start node s start Results of the calculation between), the parent pointer of the neighborhood node is pointed to the current node;
5) If the current nodeTarget node s exists in the neighborhood node of (a) end The procedure jumps to step (7); otherwise, jumping to the step (4);
6) Algorithm search fails, there is no start point s start To target point s end Is the shortest path of (a);
7) The algorithm search ends, from the starting point s start To target point s end And can be transmitted from the target node s based on the parent node information end The shortest path is obtained by the departure estimation.
8) If the subsequent simulation scene is changed and the assembly path of the loading part is influenced, the collision prevention path of the loading part needs to be planned again.
Preferably, in another embodiment of the present application, after the step of calculating the collision avoidance assembly path of the collision avoidance bounding box based on the a-Star algorithm and assembling the collision avoidance bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path, the method specifically includes the steps of:
acquiring an assembly change demand, and detecting whether assembly constraints exist in the assembly simulation knowledge graph according to the assembly change demand;
if strong assembly constraints exist, suspending assembly changes; if weak assembly constraint exists, executing assembly change and prompting risk change; if no assembly constraints exist, an assembly change is performed.
Specifically, in this embodiment, the current production requirements of the product gradually change towards small batches and multiple batches, customization, etc., and the production process of the product often faces the situation that the product needs to be adjusted for different models, and for this current situation, the application also designs an assembly process simulation flow reconstruction method facing assembly change requirements, which specifically comprises the following steps:
based on the existing assembly process simulation flow, different assembly change requirements are oriented, including assembly resource change, assembly sequence change and process information change, and different responses are carried out:
1) The change of the content of the assembly resource is mainly represented as the change of an assembly model in the assembly process simulation flow, including the change of tool tools and a loading part model, so that the main detection content is whether the changed assembly model can interfere with the subsequent assembly simulation flow;
2) The assembly sequence is changed, and the assembly sequence is mainly represented as the change of the assembly sequence of the model in the assembly process simulation flow, so that besides the model interference detection, whether the changed assembly sequence is established needs to be detected firstly, whether the model with the assembly sequence required to be changed has assembly constraint or not and whether the assembly constraint can be destroyed by changing the assembly sequence is inquired through an assembly simulation knowledge graph, and meanwhile, the assembly constraint is shown in fig. 6;
3) In the three-dimensional assembly simulation, the process information is mainly in a text form in a scene or a prompt box, and the assembly model is not changed, so that the process information is mainly changed in the way that the process information to be changed corresponds to a simulation flow link, and then the process information is added to the corresponding assembly process simulation flow link in the text form.
Referring to fig. 7, the embodiment of the application further provides an assembly simulation planning system based on a knowledge graph, which includes:
the acquisition module is used for acquiring the loading part model and constructing an assembly simulation knowledge graph;
the simulation scene construction module is in communication connection with the acquisition module and is used for constructing a three-dimensional assembly simulation scene according to the assembly simulation knowledge graph and the assembly simulation model;
the packaging box construction module is in communication connection with the simulation scene construction module and is used for constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm; the method comprises the steps of,
the path planning module is in communication connection with the packaging box construction module and is used for calculating a collision prevention assembly path of the collision bounding box based on an A-Star algorithm and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision prevention assembly path.
The assembly change module is in communication connection with the path planning module and is used for acquiring an assembly change demand and detecting whether assembly constraint exists in the assembly simulation knowledge graph according to the assembly change demand; if strong assembly constraints exist, suspending assembly changes; if weak assembly constraint exists, executing assembly change and prompting risk change; if no assembly constraints exist, an assembly change is performed.
Therefore, the method drives the construction of a three-dimensional assembly simulation scene by constructing an assembly simulation knowledge graph, and constructs a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm; and calculating a collision avoidance assembly path of the collision bounding box based on an A-Star algorithm, and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path. Meanwhile, according to the existing assembly process flow and the assembly path of the loading piece, an initial assembly simulation process flow is designed, and quick reconstruction of the assembly simulation process flow is realized by responding to different assembly change requirements so as to complete assembly simulation planning. Therefore, the assembly process planning simulation can be realized, the assembly site is guided in a visual mode, and the accuracy and the efficiency of the assembly process simulation are improved.
Specifically, the present embodiment corresponds to the foregoing method embodiments one by one, and the functions of each module are described in detail in the corresponding method embodiments, so that a detailed description is not given.
Based on the same inventive concept, the embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, which when being executed by a processor implements all or part of the method steps of the above method.
The present application may be implemented by implementing all or part of the above-described method flow, or by instructing the relevant hardware by a computer program, which may be stored in a computer readable storage medium, and which when executed by a processor, may implement the steps of the above-described method embodiments. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, executable files or in some intermediate form, etc. The computer readable medium may include: any entity or device capable of carrying computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the content of the computer readable medium can be appropriately increased or decreased according to the requirements of the jurisdiction's jurisdiction and the patent practice, for example, in some jurisdictions, the computer readable medium does not include electrical carrier signals and telecommunication signals according to the jurisdiction and the patent practice.
Based on the same inventive concept, the embodiment of the application also provides an electronic device, which comprises a memory and a processor, wherein the memory stores a computer program running on the processor, and the processor executes the computer program to realize all or part of the method steps in the method.
The processor may be a central processing unit (Central Processing Unit, CPU), other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, the processor being a control center of the computer device, and the various interfaces and lines connecting the various parts of the overall computer device.
The memory may be used to store computer programs and/or modules, and the processor implements various functions of the computer device by running or executing the computer programs and/or modules stored in the memory, and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function (e.g., a sound playing function, an image playing function, etc.); the storage data area may store data (e.g., audio data, video data, etc.) created according to the use of the handset. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, server, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, magnetic disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), servers and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present application without departing from the spirit or scope of the application. Thus, it is intended that the present application also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (10)
1. The assembly simulation planning method based on the knowledge graph is characterized by comprising the following steps of:
acquiring a loading part model and constructing an assembly simulation knowledge graph;
constructing a three-dimensional assembly simulation scene according to the loading part model and the assembly simulation knowledge graph;
constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm;
and calculating a collision avoidance assembly path of the collision bounding box based on an A-Star algorithm, and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path.
2. The knowledge-graph-based assembly simulation planning method according to claim 1, wherein the step of constructing an assembly simulation knowledge graph comprises the following steps:
acquiring assembly process knowledge data, converting the assembly process knowledge data into natural language, converting the natural language into a csv format file through a knowledge extraction technology, and storing knowledge extraction results in a Neo4j graph database to obtain an assembly simulation knowledge graph.
3. The knowledge-graph-based assembly simulation planning method according to claim 1, wherein the step of constructing a three-dimensional assembly simulation scene according to the assembly model and the assembly simulation knowledge graph comprises the following steps:
and acquiring actual assembly scene information of the loading part according to the loading part model, and mapping the actual assembly scene information into a three-dimensional assembly simulation scene through the assembly simulation knowledge graph.
4. The knowledge-graph-based assembly simulation planning method according to claim 1, wherein the step of constructing the collision bounding box of the insert model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm specifically comprises the following steps:
and taking the central point of the loading part model as a base point, obtaining the maximum value and the minimum value of the loading part model in the three axial directions of the three-dimensional assembly simulation scene coordinate system x, y and z, determining eight vertex coordinates of a collision bounding box of the loading part model according to the maximum value and the minimum value in each direction, and constructing the collision bounding box of the loading part model according to the eight vertex coordinates of the collision bounding box.
5. The knowledge-graph-based assembly simulation planning method according to claim 1, wherein the step of calculating the collision avoidance assembly path of the collision bounding box based on the a-Star algorithm comprises the following steps:
step one, determining a starting node and a target node of a collision avoidance assembly path of the collision bounding box, and calculating cost estimation values of all collision-free neighborhood nodes of the starting node;
searching a collision-free neighborhood node corresponding to the minimum cost estimation value as a current node, and calculating cost estimation values of all collision-free neighborhood nodes of the current node;
step three, repeating the step two until the collision-free neighborhood node corresponding to the minimum cost estimation value is the target node, and ending the new current point search;
and step four, obtaining a collision avoidance assembly path of the collision bounding box according to the initial node, the target node and all the searched current nodes.
6. The knowledge-graph-based assembly simulation planning method according to claim 1, wherein after the step of calculating a collision avoidance assembly path of the collision bounding box based on the a-Star algorithm and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision avoidance assembly path, the method specifically comprises the following steps:
acquiring an assembly change demand, and detecting whether assembly constraints exist in the assembly simulation knowledge graph according to the assembly change demand;
if strong assembly constraints exist, suspending assembly changes; if weak assembly constraint exists, executing assembly change and prompting risk change; if no assembly constraints exist, an assembly change is performed.
7. An assembly simulation planning system based on a knowledge graph is characterized by comprising:
the acquisition module is used for acquiring the loading part model and constructing an assembly simulation knowledge graph;
the simulation scene construction module is in communication connection with the acquisition module and is used for constructing a three-dimensional assembly simulation scene according to the assembly simulation knowledge graph and the assembly simulation model;
the packaging box construction module is in communication connection with the simulation scene construction module and is used for constructing a collision bounding box of the loading part model in the three-dimensional assembly simulation scene based on an AABB bounding box algorithm; the method comprises the steps of,
the path planning module is in communication connection with the packaging box construction module and is used for calculating a collision prevention assembly path of the collision bounding box based on an A-Star algorithm and assembling the collision bounding box into the three-dimensional assembly simulation scene according to the collision prevention assembly path.
8. The knowledge-graph-based assembly simulation planning system of claim 7, further comprising an assembly change module in communication with the path planning module for obtaining an assembly change demand, detecting whether an assembly constraint exists in the assembly simulation knowledge graph based on the assembly change demand; if strong assembly constraints exist, suspending assembly changes; if weak assembly constraint exists, executing assembly change and prompting risk change; if no assembly constraints exist, an assembly change is performed.
9. A storage medium having stored thereon a computer program, which when executed by a processor implements the knowledge-graph based assembly simulation planning method of any of claims 1 to 6.
10. An electronic device comprising a storage medium, a processor and a computer program stored in the storage medium and executable on the processor, characterized in that the processor implements the knowledge-graph based assembly simulation planning method according to any one of claims 1 to 6 when the computer program is executed by the processor.
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| CN202311204169.4A CN117236020A (en) | 2023-09-15 | 2023-09-15 | Knowledge graph-based assembly simulation planning method, system, medium and equipment |
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