CN117612145A - Automatic part machining method and device, computer equipment and storage medium - Google Patents
Automatic part machining method and device, computer equipment and storage medium Download PDFInfo
- Publication number
- CN117612145A CN117612145A CN202311737846.9A CN202311737846A CN117612145A CN 117612145 A CN117612145 A CN 117612145A CN 202311737846 A CN202311737846 A CN 202311737846A CN 117612145 A CN117612145 A CN 117612145A
- Authority
- CN
- China
- Prior art keywords
- data
- processing
- graph
- optimal
- area
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 122
- 238000003860 storage Methods 0.000 title claims abstract description 21
- 238000003754 machining Methods 0.000 title claims description 71
- 238000012545 processing Methods 0.000 claims abstract description 224
- 230000008569 process Effects 0.000 claims abstract description 90
- 238000010586 diagram Methods 0.000 claims description 47
- 239000011159 matrix material Substances 0.000 claims description 43
- 230000006870 function Effects 0.000 claims description 28
- 230000015654 memory Effects 0.000 claims description 27
- 238000006243 chemical reaction Methods 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 2
- 238000003672 processing method Methods 0.000 abstract description 25
- 238000010276 construction Methods 0.000 description 9
- 230000005012 migration Effects 0.000 description 8
- 238000013508 migration Methods 0.000 description 8
- 230000007935 neutral effect Effects 0.000 description 5
- 238000004519 manufacturing process Methods 0.000 description 4
- 230000008859 change Effects 0.000 description 2
- 238000005553 drilling Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000007787 solid Substances 0.000 description 2
- 238000004364 calculation method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 210000001503 joint Anatomy 0.000 description 1
- 238000002372 labelling Methods 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010295 mobile communication Methods 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 238000000059 patterning Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
- G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
- G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of automatic processing, and discloses an automatic part processing method, an automatic part processing device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring part selection area data, and converting the part selection area data into processing sheet area data; the processing area is a complex feature combination area; acquiring part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed; and generating part processing data based on the optimal processing sheet area template, and automatically processing the part to be processed based on the part processing data. The invention avoids single feature recognition of complex feature combination and cross feature, solves the problems of difficult recognition of cross feature and complex combination feature area and difficult process decision, realizes accurate processing of the part to be processed, and improves the processing efficiency of the part.
Description
Technical Field
The invention relates to the technical field of automatic machining, in particular to an automatic part machining method, an automatic part machining device, computer equipment and a storage medium.
Background
In the field of automatic processing, the processing scheme of an automatic processing system comprises: firstly, performing processing feature identification by using a method based on attribute adjacency graph (Attribute Adjacency Graph, abbreviated as AAG) matching, wherein the processing features comprise simple features such as processing requirement holes, grooves and the like, and providing information such as dimension parameters of the processing features; secondly, according to the information such as the type and the size of the feature, a process scheme and processing parameters of the feature to be processed are retrieved from a process rule base; finally, according to the information of the processing scheme, the processing parameters, the cutter and the like, the program group is automatically generated in CAM (Computer Aided Manufacturing ) software.
The related parts to be processed often contain a large number of complex feature combination areas, but the processing scheme can only identify simple features of the parts, so that the identification of the cross features and the complex feature combination areas is difficult, the process decision is difficult to construct a rule base and give a procedure scheme, and CAM automatic programming cannot be performed, so that the processing of the parts containing a large number of complex feature combination areas is difficult.
Disclosure of Invention
In view of the above, the present invention provides an automatic processing method, apparatus, computer device and storage medium for parts, so as to solve the problem that the related processing scheme is difficult to process parts including a large number of complex feature combination areas.
In a first aspect, the present invention provides a method for automatically machining a part, the method comprising:
acquiring part selection area data, and converting the part selection area data into processing sheet area data; the processing area is a complex feature combination area;
acquiring part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed;
and generating part processing data based on the optimal processing sheet area template, and automatically processing the part to be processed based on the part processing data.
The automatic part machining method provided by the embodiment defines a machining area comprising a large number of complex feature combination areas as a machining area, and the machining area is taken as a whole, so that single feature recognition of complex feature combination and cross features is avoided, feature recognition efficiency of part selection area data is improved, optimal matching of a part area to be machined and the machining area is realized by utilizing an optimal graph matching algorithm, further, automatic machining is performed on the part to be machined based on the part machining data, and the problems of difficult cross feature recognition, difficult process decision and difficult automatic CAM programming are solved, accurate machining in the part to be machined is realized, and the part machining efficiency is improved.
In an alternative embodiment, converting part selection area data into tooling slice data includes:
determining geometric data, topology data and process data based on the part selection area data;
constructing a graph data structure based on the geometric data and the topology data;
and determining processing zone data based on the graph data structure and the process data, and storing the processing zone data into a processing zone process knowledge base.
According to the automatic part processing method provided by the embodiment, aiming at the complex feature combination area and the cross feature area in the part selection area, the geometric data, the topology data and the process data are extracted, the graph data structure is built based on the geometric data and the topology data, the processing region data are determined based on the graph data structure and the process data, the weakness of an automatic processing system based on single feature recognition when facing the cross feature and the complex feature combination is solved, meanwhile, any complex feature template/processing region template is quickly recorded, the workload of automatically processing and recording the feature template is reduced, and the processing region template of any combination in a processing region process knowledge base is quickly expanded.
In an alternative embodiment, constructing a graph data structure based on geometric data and topology data includes:
Determining part geometric entities based on the geometric data, and taking the part geometric entities as source map nodes;
determining connection relations among geometric entities of the parts based on the topology data, and taking the connection relations among the geometric entities of the parts as source graph node connection relations;
and constructing a graph data structure based on the connection relation between the source graph nodes and the source graph nodes.
According to the automatic part processing method provided by the embodiment, the node-node relationship in the processing area is identified, namely, the part geometric entities are used as source diagram nodes, and the connection relationship between the part geometric entities is used as the source diagram node connection relationship, so that the rapid construction of the diagram data structure corresponding to the processing area is realized, and the identification efficiency and accuracy of the characteristics of the processing area are improved.
In an alternative embodiment, determining an optimal tooling lot template using an optimal map matching algorithm based on tooling lot data and part map data to be machined includes:
constructing an optimal graph matching objective function based on the processing area data and the to-be-processed part graph data by taking the node corresponding relation as an objective;
solving the optimal graph matching objective function to generate an optimal graph data structure;
process data corresponding to the optimal graph data structure is called from a processing region process knowledge base;
And generating an optimal processing sheet area template based on the optimal diagram data structure and the process data corresponding to the optimal diagram data structure.
According to the automatic part processing method, the optimal graph matching objective function is used for realizing rapid matching of the template of the optimal processing sheet area, the graph matching efficiency is improved, the problem of identifying partial similar areas of a large number of parts in a factory is solved, and a foundation is laid for process migration and reuse of the areas of the parts to be processed in the follow-up process.
In an alternative embodiment, with the node correspondence being the optimal target, constructing an optimal graph matching objective function based on the machining region data and the to-be-machined part graph data, including:
determining a connection relation between a target graph node and the target graph node based on the to-be-processed part graph data, and constructing a target graph adjacency matrix based on the connection relation between the target graph node and the target graph node;
constructing a source graph adjacency matrix based on the connection relation between the source graph nodes and the source graph nodes;
and constructing an optimal graph matching objective function based on the graph data structure, the part graph data to be processed, the target graph adjacent matrix and the source graph adjacent matrix.
According to the automatic part processing method, the judgment of the node corresponding relation is achieved through the adjacent matrix, the optimal graph matching objective function is built based on the graph data structure, the to-be-processed part graph data, the target graph adjacent matrix and the source graph adjacent matrix, the optimal node corresponding relation is searched and calculated through the optimal graph matching objective function, and therefore the rapid matching of the optimal processing sheet area template is achieved.
In an alternative embodiment, a best-fit graph matching objective function is constructed based on the graph data structure, the part graph data to be processed, the target graph abutment matrix, and the source graph abutment matrix, the best-fit graph matching objective function being expressed as follows:
in the above, V S Representing node sets in a source graph, V t Represents a node set in the target graph, S represents a node corresponding relationship,representing source graph adjacency matrix, ">Representing a target graph adjacency matrix, S i,j Representing the corresponding relation between the source graph node i and the target graph node j, S i',j' Representation ofCorrespondence between source graph node i 'and target graph node j'.
In an alternative embodiment, generating part machining data based on the optimal machining zone template and automatically machining the part to be machined based on the part machining data includes:
determining part processing data of the part to be processed based on the optimal diagram data structure and process data corresponding to the optimal diagram data structure;
and determining a machining coordinate system of a part matching area to be machined based on the part machining data, and automatically machining the part to be machined based on the part machining data and the machining coordinate system.
According to the automatic part machining method provided by the embodiment, the part machining data of the part to be machined is determined through the optimal diagram data structure and the corresponding process data, and then the part to be machined is automatically machined based on the part machining data and the machining coordinate system, so that the multiplexing of the whole process of machining the sheet area is realized, the partial similar part processes are migrated and multiplexed, a great number of repeated works of users are avoided, and the machining efficiency of the part to be machined is improved.
In a second aspect, the present invention provides an automatic part machining apparatus comprising:
the conversion module is used for acquiring the data of the part selection area and converting the data of the part selection area into the data of the processing sheet area; the processing area is a complex feature combination area;
the determining module is used for acquiring the part drawing data to be processed, and determining an optimal processing area template by utilizing an optimal drawing matching algorithm based on the processing area data and the part drawing data to be processed;
and the processing module is used for generating part processing data based on the optimal processing sheet area template and automatically processing the part to be processed based on the part processing data.
In a third aspect, the present invention provides a computer device comprising: the automatic part machining device comprises a memory and a processor, wherein the memory and the processor are in communication connection, the memory stores computer instructions, and the processor executes the computer instructions, so that the automatic part machining method according to the first aspect or any corresponding embodiment of the first aspect is executed.
In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute a method of automatic part machining according to the first aspect or any of the embodiments corresponding thereto.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of automated part machining according to an embodiment of the present invention;
FIG. 2 is a flow chart of another method of automated part machining according to an embodiment of the present invention;
FIG. 3 is a flow chart of yet another method of automated part machining in accordance with an embodiment of the present invention;
FIG. 4 is a flow chart of yet another method of automated part machining in accordance with an embodiment of the present invention;
FIG. 5 is a schematic flow diagram of automated part processing using an automated processing system in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of a process patch quick entry in accordance with an embodiment of the present invention;
FIG. 7 is a schematic diagram of process multiplex migration in accordance with an embodiment of the invention;
FIG. 8 is a block diagram of an automatic part processing apparatus according to an embodiment of the present invention;
Fig. 9 is a schematic diagram of a hardware structure of a computer device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The processing scheme of the automatic processing system has the following problems: firstly, a large amount of time is consumed manually for manually manufacturing a characteristic template for each characteristic processing template, experienced users and process personnel are often required to be in butt joint with developers in actual engineering application, and a series of special rules are developed to identify the characteristic processing characteristics of the users; for complex cross features (such as a plurality of holes are mutually crossed, an open slot is combined with various features, and the like), time and effort are also difficult to fully identify, and a template is often a simple feature template (such as a hole, a slot, and the like) after the template is manufactured, so that the situation that a large number of combined features cannot be covered (such as a plurality of slots and holes unique to a customer are combined together in a staggered manner) can not be covered; secondly, the technological rule base which needs to be constructed for technological scheme decision is limited in coverage, and for various complex cross characteristics, complex rule surfaces cannot be set for each situation, and the complex cross combination characteristics are not enough.
The embodiment of the invention provides an automatic part processing method which is applied to server equipment and realizes rapid entry of a template of any complex processing area and integral identification and process multiplexing of any complex processing area by extracting part information and process information of the whole processing area and multiplexing in other similar areas of any part.
In accordance with an embodiment of the present invention, there is provided an automatic part machining method embodiment, it being noted that the steps shown in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order other than that shown or described herein.
In this embodiment, an automatic part processing method is provided, which may be used in the server device described above, and fig. 1 is a flowchart of an automatic part processing method according to an embodiment of the present invention, as shown in fig. 1, where the flowchart includes the following steps:
step S101, acquiring part selection area data, and converting the part selection area data into processing sheet area data; wherein the processing area is a complex feature combination area.
Specifically, for the problem that the automatic processing based on the features cannot meet the requirements of users, a processing area is defined as a processing area, wherein the processing area comprises a large number of complex feature combination areas, namely various features which are combined together in a crossed manner or are formed by simple arrangement, and the areas have certain technological significance.
Further, the processing area is an area with specific processing characteristics, and is composed of a group of surfaces, edges, attributes and a plurality of processing methods which are arranged in sequence.
Further, all the geometric, topological, attribute, processing method and other information in the part selection area data selected by the user are extracted, the information is converted into neutral diagram data information and process information, and the neutral diagram data information, the process information and the preset processing area name are stored in a processing area process knowledge base.
Further, the region data is selected for a plurality of parts selected by a user, converted into processed region data, and a processed region template for a plurality of part selected regions is constructed based on the processed region data.
Step S102, obtaining the part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed.
Specifically, the to-be-machined part graph data is an attribute adjacency graph, and an optimal graph matching algorithm is adopted to obtain an optimal matching result of a machining area template defined by a user on the to-be-machined part.
And step S103, generating part processing data based on the optimal processing area template, and automatically processing the part to be processed based on the part processing data.
Specifically, the process in the optimal processing sheet area template is multiplexed into one or more matching areas in the part to be processed, and the processing such as the completion of geometric information, the recalculation of height thickness information and the like is carried out according to the data of the matching areas of the part to be processed.
The automatic part machining method provided by the embodiment defines a machining area comprising a large number of complex feature combination areas as a machining area, and the machining area is taken as a whole, so that single feature recognition of complex feature combination and cross features is avoided, feature recognition efficiency of part selection area data is improved, optimal matching of a part area to be machined and the machining area is realized by utilizing an optimal graph matching algorithm, further, automatic machining is performed on the part to be machined based on the part machining data, and the problems of difficult cross feature recognition, difficult process decision and difficult automatic CAM programming are solved, accurate machining in the part to be machined is realized, and the part machining efficiency is improved.
In this embodiment, an automatic part processing method is provided, which may be used for the server device and the like, and fig. 2 is a flowchart of an automatic part processing method according to an embodiment of the present invention, and as shown in fig. 2, the flowchart includes the following steps:
step S201, acquiring part selection area data, and converting the part selection area data into processing sheet area data; wherein the processing area is a complex feature combination area.
Specifically, the step S201 includes:
in step S2011, geometric data, topology data, and process data are determined based on the part selection area data.
Specifically, all the part information, process information, user labeling information and PMI (Product Manufacturing Information ) in the part selection area data selected by the user are extracted, wherein the part information comprises geometric data and topology data.
Step S2012, constructing a graph data structure based on the geometric data and the topology data.
In some alternative embodiments, step S2012 includes:
and a step a1, determining part geometric entities based on the geometric data, wherein the part geometric entities are used as source map nodes.
Specifically, the part geometry entity includes each face and side of the part in the geometry data.
Further, geometric data, PMI information and the like of each face and each side are used as attributes of source graph nodes, such as face types, maximum curvatures, minimum curvatures and the like corresponding to the face nodes, and edge types, lengths, parameter equations, parameter ranges and the like corresponding to the side nodes.
And a step a2, determining the connection relation between the geometric entities of the parts based on the topology data, and taking the connection relation between the geometric entities of the parts as a source graph node connection relation.
And a step a3, constructing a graph data structure based on the connection relation between the source graph nodes and the source graph nodes.
According to the automatic part processing method provided by the embodiment, the node-node relationship in the processing area is identified, namely, the part geometric entities are used as source diagram nodes, and the connection relationship between the part geometric entities is used as the source diagram node connection relationship, so that the rapid construction of the diagram data structure corresponding to the processing area is realized, and the identification efficiency and accuracy of the characteristics of the processing area are improved.
Step S2013, determining processing area data based on the graph data structure and the process data, and storing the processing area data into a processing area process knowledge base.
Specifically, the process data such as the overall process, the processing sequence, the processing scheme and the like of the processing sheet area are used as the additional information of the graph data structure, such as the information of drilling, the spindle rotation speed 5000 and the like, and are used as the additional information of the graph data structure for the subsequent process migration.
Step S202, obtaining the part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed. Please refer to step S102 in the embodiment shown in fig. 1 in detail, which is not described herein.
And step S203, generating part processing data based on the optimal processing area template, and automatically processing the part to be processed based on the part processing data. Please refer to step S103 in the embodiment shown in fig. 1 in detail, which is not described herein.
According to the automatic part processing method provided by the embodiment, aiming at the complex feature combination area and the cross feature area in the part selection area, the geometric data, the topology data and the process data are extracted, the graph data structure is built based on the geometric data and the topology data, the processing region data are determined based on the graph data structure and the process data, the weakness of an automatic processing system based on single feature recognition when facing the cross feature and the complex feature combination is solved, meanwhile, any complex feature template/processing region template is quickly recorded, the workload of automatically processing and recording the feature template is reduced, and the processing region template of any combination in a processing region process knowledge base is quickly expanded.
In this embodiment, an automatic part processing method is provided, which may be used in the server device described above, and fig. 3 is a flowchart of an automatic part processing method according to an embodiment of the present invention, as shown in fig. 3, where the flowchart includes the following steps:
step S301, acquiring part selection area data, and converting the part selection area data into processing sheet area data; wherein the processing area is a complex feature combination area. Please refer to step S201 in the embodiment shown in fig. 2 in detail, which is not described herein.
Step S302, obtaining the part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed.
Specifically, the step S302 includes:
step S3021, constructing an optimal graph matching objective function based on the machining region data and the to-be-machined part graph data with the optimal node correspondence relationship as a target.
In some alternative embodiments, step S3021 includes:
and b1, determining a connection relation between a target graph node and the target graph node based on the to-be-processed part graph data, and constructing a target graph adjacency matrix based on the connection relation between the target graph node and the target graph node.
Specifically, the target graph nodes are used as rows and columns of the adjacency matrix, matrix element values are determined according to the connection relation of the target graph nodes, and then the target graph adjacency matrix is built based on the rows, the columns and the matrix element values.
And b2, constructing a source graph adjacency matrix based on the connection relation between the source graph nodes and the source graph nodes.
Specifically, the construction process of the source map adjacency matrix is the same as that of the target map adjacency matrix.
And b3, constructing an optimal graph matching objective function based on the graph data structure, the part graph data to be processed, the target graph adjacent matrix and the source graph adjacent matrix.
Specifically, the expression of the optimal graph matching objective function is as follows:
in the above, V S Representing node sets in a source graph, V t Represents a node set in the target graph, S represents a node corresponding relationship,representing source graph adjacency matrix, ">Representing a target graph adjacency matrix, S i,j Representing the corresponding relation between the source graph node i and the target graph node j, S i',j' The correspondence between the source graph node i 'and the target graph node j' is represented.
Wherein the adjacency matrix represents a matrix of connection relations between nodes, S is a matrix corresponding to the node correspondence relation,
according to the automatic part processing method, the judgment of the node corresponding relation is achieved through the adjacent matrix, the optimal graph matching objective function is built based on the graph data structure, the to-be-processed part graph data, the target graph adjacent matrix and the source graph adjacent matrix, the optimal node corresponding relation is searched and calculated through the optimal graph matching objective function, and therefore the rapid matching of the optimal processing sheet area template is achieved.
Step S3022, solving the optimal graph matching objective function to generate an optimal graph data structure.
Specifically, the target graph V corresponding to the part to be processed is firstly displayed t The template diagram of the processing area is calculated and obtained by the above steps (namely, a source diagram V S ) Is a pre-match result of { S 1 ,S 2 ,...,S N The matching result of N processing region templates found on Vt, S i Representing the corresponding relation between the original image and the node in the target image in the ith matching result; and (3) after the pre-matching result is obtained, optimizing through the formula (1) to ensure that the node corresponding relation is optimal, obtaining the optimal node corresponding relation of each matching result, and further determining an optimal graph data structure according to the optimal node corresponding relation.
In step S3023, process data corresponding to the optimal graph data structure is retrieved from the process knowledge base of the processing region.
Step S3024, generating an optimal processing area template based on the optimal map data structure and the process data corresponding to the optimal map data structure.
Step S303, generating part processing data based on the optimal processing area template, and automatically processing the part to be processed based on the part processing data. Please refer to step S203 in the embodiment shown in fig. 2 in detail, which is not described herein.
According to the automatic part processing method, the optimal graph matching objective function is used for realizing rapid matching of the template of the optimal processing sheet area, the graph matching efficiency is improved, the problem of identifying partial similar areas of a large number of parts in a factory is solved, and a foundation is laid for process migration and reuse of the areas of the parts to be processed in the follow-up process.
In this embodiment, an automatic part processing method is provided, which may be used in the server device described above, and fig. 4 is a flowchart of an automatic part processing method according to an embodiment of the present invention, as shown in fig. 4, where the flowchart includes the following steps:
step S401, acquiring part selection area data, and converting the part selection area data into processing sheet area data; wherein the processing area is a complex feature combination area. Please refer to step S301 in the embodiment shown in fig. 3 in detail, which is not described herein.
Step S402, obtaining the part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed. Please refer to step S302 in the embodiment shown in fig. 3 in detail, which is not described herein.
Step S403, generating part processing data based on the optimal processing area template, and automatically processing the part to be processed based on the part processing data.
Specifically, the step S403 includes:
step S4031, part processing data of the part to be processed is determined based on the optimal diagram data structure and the process data corresponding to the optimal diagram data structure.
Step S4032, determining a machining coordinate system of the matching area of the part to be machined based on the part machining data, and automatically machining the part to be machined based on the part machining data and the machining coordinate system.
Specifically, according to the optimal matching result and the node corresponding relation, the process on the processing sheet area template is migrated to the matching area of the part to be processed, and the replacement of driving geometric information in the process and the recalculation of processing parameters (processing parameters of main shaft rotating speed, feeding rate and the like along with the change of driving geometric) are completed, so that the process migration is realized.
Further, a new processing coordinate system is established for the process data after the matching area of the part to be processed is updated, so that the whole process migration of the processing area in any complex and arbitrary direction is completed.
According to the automatic part machining method provided by the embodiment, the part machining data of the part to be machined is determined through the optimal diagram data structure and the corresponding process data, and then the part to be machined is automatically machined based on the part machining data and the machining coordinate system, so that the multiplexing of the whole process of machining the sheet area is realized, the partial similar part processes are migrated and multiplexed, a great number of repeated works of users are avoided, and the machining efficiency of the part to be machined is improved.
The following describes specific steps of an automatic processing method for parts by means of specific examples.
Example 1:
aiming at the problem that the traditional automatic processing based on the characteristics can not meet the demands of users, the embodiment provides the concept of processing a sheet area, namely a certain sheet has the cross combination area of various characteristics with certain technological significance; the parts often comprise a large number of complex feature combination areas, namely various processing areas, such as various features are combined together in a crossed manner, or certain areas with certain technological significance are formed by simple arrangement, so that an automatic processing system is provided for the processing areas, and the whole recognition and technological multiplexing of the template of any complex processing area and any complex processing area are realized by extracting the whole part information and technological information of the processing areas and multiplexing the whole part information and the technological information in other similar areas of any part; the automatic processing system is composed of a rapid processing area input system and a rapid processing area process multiplexing system, as shown in fig. 5, the specific steps of using the automatic processing system to automatically process the parts include:
The first processing area is an area with specific processing technology characteristics and consists of a group of surfaces, edges, attributes and a plurality of processing methods which are arranged in sequence, and the processing area can be an area formed by freely combining, crossing, connecting and separating a plurality of traditional characteristics.
Step two, as shown in fig. 6, starting a processing sheet area rapid entry system, extracting all information of a part selection area through the processing sheet area rapid entry system, and patterning: acquiring the data of the interested area selected by the user, namely the part selecting area, through a user selecting interface, and inputting the custom name of the processing area; and extracting all the information such as geometry, topology, attributes, processing methods and the like from the data of the selected area of the part, converting the information into neutral diagram data information (namely diagram data structure) and process information, and storing the neutral diagram data information (namely diagram data structure) and the process information into a knowledge base of the processed area, so that the new characteristic entering process is completed.
The neutral diagram data information corresponds to processing area data in the part model selected by a user, each node in the diagram data structure corresponds to each face and side geometrical entity in the part model, and the sides in the diagram correspond to connection relations among the geometrical entities. The geometrical information, PMI information and the like of each face and each side are used as attributes of the node, such as the face type, the maximum curvature, the minimum curvature and the like corresponding to the face node, and the side node corresponds to the side type, the length, the parameter equation, the parameter range and the like; and finally, taking the process information such as the whole process, the processing sequence, the processing scheme and the like of the processing sheet area as the additional information of the drawing, wherein the process information comprises the information such as drilling, spindle rotation speed 5000 and the like for subsequent process migration.
Step three, as shown in fig. 7, starting a 'processing sheet area process rapid multiplexing system', and calculating optimal graph matching through the processing sheet area process rapid multiplexing system: graphically displaying an input processing region type list, selecting a required processing region type by a user, and clicking for determination; and then calculating an optimal graph matching result on the part to be processed, wherein the part to be processed is a large attribute adjacent graph, and the optimal graph matching result of the processing sheet area template on the part to be processed is calculated by adopting an optimal graph matching algorithm, and the core of the optimal graph matching algorithm is to solve the following secondary assignment problem of the maintaining edges, wherein the calculation formula is as follows:
the optimal diagram matching algorithm firstly generates a target diagram V corresponding to the part to be processed t The template diagram of the processing area is calculated and obtained by the above steps (namely, a source diagram V S ) Is a pre-match result of { S 1 ,S 2 ,...,S N The matching result of N processing region templates found on Vt, S i Representing the corresponding relation between the original image and the node in the target image in the ith matching result; and (3) after the pre-matching result is obtained, optimizing through the formula (1) to ensure that the node corresponding relation is optimal, obtaining the optimal node corresponding relation of each matching result, and further determining an optimal graph data structure according to the optimal node corresponding relation.
And step four, migrating the process on the processing sheet area template to a matching area according to the optimal matching result and the node corresponding relation, completing the replacement of driving geometric information in the process and the recalculation of process processing parameters (processing parameters of spindle rotating speed, feeding rate and the like along with the driving geometric change), and realizing the process migration.
In the embodiment, the problem that the traditional feature recognition and process decision are difficult to add a feature template and face a huge amount of complex features, cross features and feature combination areas is considered, the situation cannot be well processed by the traditional automatic processing method, and on the basis of the thought of integral processing of the processing area, the method introduces integral learning and multiplexing of all information of the processing area, realizes quick feature/processing area template input, solves the problems of difficult cross features and complex combination feature area recognition, difficult process decision and difficult automatic CAM programming at one time, and opens up a new road in the automatic processing field.
In this embodiment, an automatic part machining device is further provided, and the device is used to implement the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides an automatic part processing apparatus, as shown in fig. 8, including:
the conversion module 801 is configured to obtain part selection area data, and convert the part selection area data into processed sheet area data; the processing area is a complex feature combination area;
the determining module 802 is configured to obtain to-be-processed part drawing data, and determine an optimal processing area template by using an optimal drawing matching algorithm based on the processing area data and the to-be-processed part drawing data;
and the processing module 803 is used for generating part processing data based on the optimal processing area template and automatically processing the part to be processed based on the part processing data.
In some alternative embodiments, conversion module 801 includes:
a first determining unit for determining geometric data, topology data and process data based on the part selection area data;
a first construction unit for constructing a graph data structure based on the geometric data and the topology data;
and the storage unit is used for determining the processing region data based on the graph data structure and the process data and storing the processing region data into a processing region process knowledge base.
In some alternative embodiments, the first building element comprises:
A first determining subunit, configured to determine a part geometry entity based on the geometry data, and take the part geometry entity as a source map node;
the second determining subunit is used for determining the connection relation between the geometric entities of the parts based on the topology data, and taking the connection relation between the geometric entities of the parts as a source graph node connection relation;
and the first construction subunit is used for constructing the graph data structure based on the connection relation between the source graph nodes and the source graph nodes.
In some alternative embodiments, the determining module 802 includes:
the second construction unit is used for constructing an optimal diagram matching objective function based on the processing area data and the to-be-processed part diagram data by taking the node corresponding relation as an objective optimally;
the solving unit is used for solving the optimal graph matching objective function to generate an optimal graph data structure;
the calling unit is used for calling the process data corresponding to the optimal graph data structure from the processing region process knowledge base;
and the generating unit is used for generating an optimal processing sheet area template based on the optimal diagram data structure and the process data corresponding to the optimal diagram data structure.
In some alternative embodiments, the second building element comprises:
the second construction subunit is used for determining the connection relation between the target graph nodes and the target graph nodes based on the to-be-machined part graph data and constructing a target graph adjacency matrix based on the connection relation between the target graph nodes and the target graph nodes;
A third construction subunit, configured to construct a source graph adjacency matrix based on a connection relationship between a source graph node and a source graph node;
and a fourth construction subunit, configured to construct an optimal graph matching objective function based on the graph data structure, the part graph data to be processed, the target graph abutment matrix and the source graph abutment matrix.
In some alternative embodiments, the expression of the best graph matching objective function in the fourth building subunit is as follows:
in the above, V S Representing node sets in a source graph, V t Represents a node set in the target graph, S represents a node corresponding relationship,representing source graph adjacency matrix, ">Representing a target graph adjacency matrix, S i,j Representing the corresponding relation between the source graph node i and the target graph node j, S i',j' The correspondence between the source graph node i 'and the target graph node j' is represented.
In some alternative embodiments, the processing module 803 includes:
a second determining unit for determining part processing data of the part to be processed based on the optimal diagram data structure and the process data corresponding to the optimal diagram data structure;
and the processing unit is used for determining a processing coordinate system of the part matching area to be processed based on the part processing data and automatically processing the part to be processed based on the part processing data and the processing coordinate system.
Further functional descriptions of the above respective modules and units are the same as those of the above corresponding embodiments, and are not repeated here.
An automated part manufacturing apparatus in this embodiment is presented in the form of functional units, where the units refer to ASIC (Application Specific Integrated Circuit ) circuits, processors and memories executing one or more software or firmware programs, and/or other devices that can provide the functionality described above.
The embodiment of the invention also provides computer equipment, which is provided with the automatic part processing device shown in the figure 8.
Referring to fig. 9, fig. 9 is a schematic structural diagram of a computer device according to an alternative embodiment of the present invention, as shown in fig. 9, the computer device includes: one or more processors 10, memory 20, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are communicatively coupled to each other using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions executing within the computer device, including instructions stored in or on memory to display graphical information of the GUI on an external input/output device, such as a display device coupled to the interface. In some alternative embodiments, multiple processors and/or multiple buses may be used, if desired, along with multiple memories and multiple memories. Also, multiple computer devices may be connected, each providing a portion of the necessary operations (e.g., as a server array, a set of blade servers, or a multiprocessor system). One processor 10 is illustrated in fig. 9.
The processor 10 may be a central processor, a network processor, or a combination thereof. The processor 10 may further include a hardware chip, among others. The hardware chip may be an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general-purpose array logic, or any combination thereof.
Wherein the memory 20 stores instructions executable by the at least one processor 10 to cause the at least one processor 10 to perform the methods shown in implementing the above embodiments.
The memory 20 may include a storage program area that may store an operating system, at least one application program required for functions, and a storage data area; the storage data area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, which may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk, or solid state disk; the memory 20 may also comprise a combination of the above types of memories.
The computer device further comprises input means 30 and output means 40. The processor 10, memory 20, input device 30, and output device 40 may be connected by a bus or other means, for example by a bus connection in fig. 9.
The input device 30 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer apparatus, such as a touch screen, a keypad, a mouse, a trackpad, a touchpad, a pointer stick, one or more mouse buttons, a trackball, a joystick, and the like. The output means 40 may include a display device, auxiliary lighting means (e.g., LEDs), tactile feedback means (e.g., vibration motors), and the like. Such display devices include, but are not limited to, liquid crystal displays, light emitting diodes, displays and plasma displays. In some alternative implementations, the display device may be a touch screen.
The embodiments of the present invention also provide a computer readable storage medium, and the method according to the embodiments of the present invention described above may be implemented in hardware, firmware, or as a computer code which may be recorded on a storage medium, or as original stored in a remote storage medium or a non-transitory machine readable storage medium downloaded through a network and to be stored in a local storage medium, so that the method described herein may be stored on such software process on a storage medium using a general purpose computer, a special purpose processor, or programmable or special purpose hardware. The storage medium can be a magnetic disk, an optical disk, a read-only memory, a random access memory, a flash memory, a hard disk, a solid state disk or the like; further, the storage medium may also comprise a combination of memories of the kind described above. It will be appreciated that a computer, processor, microprocessor controller or programmable hardware includes a storage element that can store or receive software or computer code that, when accessed and executed by the computer, processor or hardware, implements the methods illustrated by the above embodiments.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
Claims (10)
1. An automatic part machining method, characterized in that the method comprises the following steps:
acquiring part selection area data, and converting the part selection area data into processing sheet area data; the processing area is a complex feature combination area;
acquiring part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed;
and generating part processing data based on the optimal processing sheet area template, and automatically processing the part to be processed based on the part processing data.
2. The method of claim 1, wherein said converting said part select area data into tooling field data comprises:
determining geometric data, topology data and process data based on the part selection area data;
constructing a graph data structure based on the geometric data and the topology data;
and determining the processing region data based on the graph data structure and the process data, and storing the processing region data into a processing region process knowledge base.
3. The method of claim 2, wherein the constructing a graph data structure based on the geometric data and the topology data comprises:
Determining a part geometric entity based on the geometric data, and taking the part geometric entity as a source map node;
determining connection relations among part geometric entities based on the topology data, and taking the connection relations among the part geometric entities as source graph node connection relations;
and constructing the graph data structure based on the connection relation between the source graph node and the source graph node.
4. A method according to claim 3, wherein said determining an optimal tooling lot template using an optimal map matching algorithm based on said tooling lot data and said part map data to be machined comprises:
constructing an optimal diagram matching objective function based on the processing area data and the to-be-processed part diagram data by taking the optimal node correspondence as a target;
solving the optimal graph matching objective function to generate an optimal graph data structure;
process data corresponding to the optimal graph data structure is called from the processing region process knowledge base;
and generating the optimal processing sheet area template based on the optimal diagram data structure and process data corresponding to the optimal diagram data structure.
5. The method of claim 4, wherein the constructing an optimal map matching objective function based on the machined segment data and the part map data with the optimal node correspondence as a target comprises:
Determining a connection relation between a target graph node and a target graph node based on the to-be-processed part graph data, and constructing a target graph adjacency matrix based on the connection relation between the target graph node and the target graph node;
constructing a source graph adjacency matrix based on the connection relation between the source graph nodes and the source graph nodes;
and constructing the optimal graph matching objective function based on the graph data structure, the to-be-machined part graph data, the target graph adjacent matrix and the source graph adjacent matrix.
6. The method of claim 5, wherein the constructing an optimal map matching objective function based on the map data structure, the part map data to be machined, the target map adjacency matrix, and the source map adjacency matrix is performed as follows:
in the above, V S Representing node sets in a source graph, V t Represents a node set in the target graph, S represents a node corresponding relationship,representing source graph adjacency matrix, ">Representing a target graph adjacency matrix, S i,j Representing the corresponding relation between the source graph node i and the target graph node j, S i',j' The correspondence between the source graph node i 'and the target graph node j' is represented.
7. The method of claim 4, wherein generating part tooling data based on the optimal tooling zone template and automatically tooling a part to be machined based on the part tooling data comprises:
Determining the part processing data of a part to be processed based on the optimal diagram data structure and process data corresponding to the optimal diagram data structure;
and determining a machining coordinate system of a part matching area to be machined based on the part machining data, and automatically machining the part to be machined based on the part machining data and the machining coordinate system.
8. An automatic part machining apparatus, characterized in that the apparatus comprises:
the conversion module is used for acquiring the part selection area data and converting the part selection area data into processing sheet area data; the processing area is a complex feature combination area;
the determining module is used for acquiring the part drawing data to be processed, and determining an optimal processing sheet area template by utilizing an optimal drawing matching algorithm based on the processing sheet area data and the part drawing data to be processed;
and the processing module is used for generating part processing data based on the optimal processing sheet area template and automatically processing the part to be processed based on the part processing data.
9. A computer device, comprising:
a memory and a processor in communication with each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the part automated process of any one of claims 1 to 7.
10. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the automatic part machining method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311737846.9A CN117612145A (en) | 2023-12-15 | 2023-12-15 | Automatic part machining method and device, computer equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311737846.9A CN117612145A (en) | 2023-12-15 | 2023-12-15 | Automatic part machining method and device, computer equipment and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117612145A true CN117612145A (en) | 2024-02-27 |
Family
ID=89944408
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311737846.9A Pending CN117612145A (en) | 2023-12-15 | 2023-12-15 | Automatic part machining method and device, computer equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117612145A (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020095276A1 (en) * | 1999-11-30 | 2002-07-18 | Li Rong | Intelligent modeling, transformation and manipulation system |
| US20060079989A1 (en) * | 2004-10-07 | 2006-04-13 | Amada Corporation, Limited | Representation of sheet metal part models |
| CN109977972A (en) * | 2019-03-29 | 2019-07-05 | 东北大学 | A kind of intelligent characteristic recognition methods based on STEP |
| CN112289172A (en) * | 2020-09-30 | 2021-01-29 | 北京德弦科技有限公司 | Gem data identification processing and detecting method and processing device |
| CN113468800A (en) * | 2021-05-27 | 2021-10-01 | 河海大学 | Complex cavity cutter combination optimization method based on genetic algorithm |
| CN114065432A (en) * | 2021-11-21 | 2022-02-18 | 西北工业大学 | Manufacturing cost estimation method based on process flow |
| CN114647741A (en) * | 2022-03-14 | 2022-06-21 | 广东技术师范大学 | Process automatic decision and reasoning method, device, computer equipment and storage medium |
| CN115169122A (en) * | 2022-07-07 | 2022-10-11 | 重庆大学 | Multi-stage manufacturing feature identification method based on graph and minimum disjoint feature volume suppression |
| CN116229116A (en) * | 2023-03-08 | 2023-06-06 | 上海优集工业软件有限公司 | Process multiplexing processing method and system based on similar parts and electronic equipment |
| CN116304227A (en) * | 2023-03-31 | 2023-06-23 | 上海优集工业软件有限公司 | Multiplexing method and system for part processing technology and electronic equipment |
| CN116403207A (en) * | 2023-04-13 | 2023-07-07 | 湖南机企猫网络科技有限公司 | Processing characteristic identification method for plate parts |
| CN116824226A (en) * | 2023-06-06 | 2023-09-29 | 长春理工大学 | Deep learning-based processing feature classification recognition method |
-
2023
- 2023-12-15 CN CN202311737846.9A patent/CN117612145A/en active Pending
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20020095276A1 (en) * | 1999-11-30 | 2002-07-18 | Li Rong | Intelligent modeling, transformation and manipulation system |
| US20060079989A1 (en) * | 2004-10-07 | 2006-04-13 | Amada Corporation, Limited | Representation of sheet metal part models |
| CN109977972A (en) * | 2019-03-29 | 2019-07-05 | 东北大学 | A kind of intelligent characteristic recognition methods based on STEP |
| CN112289172A (en) * | 2020-09-30 | 2021-01-29 | 北京德弦科技有限公司 | Gem data identification processing and detecting method and processing device |
| CN113468800A (en) * | 2021-05-27 | 2021-10-01 | 河海大学 | Complex cavity cutter combination optimization method based on genetic algorithm |
| CN114065432A (en) * | 2021-11-21 | 2022-02-18 | 西北工业大学 | Manufacturing cost estimation method based on process flow |
| CN114647741A (en) * | 2022-03-14 | 2022-06-21 | 广东技术师范大学 | Process automatic decision and reasoning method, device, computer equipment and storage medium |
| CN115169122A (en) * | 2022-07-07 | 2022-10-11 | 重庆大学 | Multi-stage manufacturing feature identification method based on graph and minimum disjoint feature volume suppression |
| CN116229116A (en) * | 2023-03-08 | 2023-06-06 | 上海优集工业软件有限公司 | Process multiplexing processing method and system based on similar parts and electronic equipment |
| CN116304227A (en) * | 2023-03-31 | 2023-06-23 | 上海优集工业软件有限公司 | Multiplexing method and system for part processing technology and electronic equipment |
| CN116403207A (en) * | 2023-04-13 | 2023-07-07 | 湖南机企猫网络科技有限公司 | Processing characteristic identification method for plate parts |
| CN116824226A (en) * | 2023-06-06 | 2023-09-29 | 长春理工大学 | Deep learning-based processing feature classification recognition method |
Non-Patent Citations (3)
| Title |
|---|
| VOLKOV, IA ; PRIMAK, DD; ARTYKH, RL; BAZHENOV, RI: "Development the assembly sequence mathematical modeling method of precision axisymmetric products based on the graph incidence matrix", INTERNATIONAL CONFERENCE ON MODERN TRENDS IN MANUFACTURING TECHNOLOGIES AND EQUIPMENT (ICMTMTE) 2020, 31 December 2020 (2020-12-31), pages 1 - 10 * |
| 刘雪梅;张树生;崔卫卫;宋加玉;: "逆向工程中基于属性邻接图的加工特征识别", 计算机集成制造系统, no. 06, 15 June 2008 (2008-06-15), pages 1 - 6 * |
| 罗云中，盛步云，陈贻堃: "基于图匹配的注塑模具加工特征识别", CNKI中国知网, 30 November 2020 (2020-11-30), pages 1 - 7 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109870903B (en) | Parameter optimization method, device and non-transitory computer readable medium | |
| CN105700888A (en) | Visualization rapid developing platform based on jbpm workflow engine | |
| US9471720B1 (en) | Combined vertical and horizontal load modeling for concrete structures | |
| CN110222407B (en) | BIM data fusion method and device | |
| US20190310608A1 (en) | Process and system for providing a machining method for manufacturing a feature in a part | |
| EP3427167B1 (en) | Method for providing details to a computer aided design (cad) model, a computer program product and a server therefore | |
| WO2022041980A1 (en) | Concept prediction to create new intents and assign examples automatically in dialog systems | |
| CN114091589A (en) | Model training method and device, electronic equipment and medium | |
| CN117612145A (en) | Automatic part machining method and device, computer equipment and storage medium | |
| CN115668074A (en) | Method and device for modeling and simulating production process | |
| CN115545577B (en) | Method and equipment for processing scheduling data | |
| JP6981296B2 (en) | Bus wiring search program, bus wiring search method and information processing device | |
| JP7309777B2 (en) | Standard time estimation device and method | |
| JPWO2019244446A1 (en) | System configuration derivation device, method and program | |
| CN114615144B (en) | Network optimization method and system | |
| CN114741072B (en) | Page generation method, device, equipment and storage medium | |
| CN115659900B (en) | Method, apparatus and medium for presenting an integrated circuit layout | |
| JP6218162B1 (en) | Semiconductor processing inspection apparatus association system and program | |
| JP5714472B2 (en) | Product information management apparatus, method, and program | |
| EP4357991A1 (en) | Use resource setting method and use resource setting device | |
| CN117875286A (en) | Data processing method, device, computer equipment and storage medium | |
| CN115934086A (en) | Information sharing method, device, equipment and medium for blueprint and page design | |
| JP2002245398A (en) | Method for preparing traveling path | |
| CN116778093A (en) | Model construction method, device, computer equipment and medium for unit engineering | |
| JPWO2017163342A1 (en) | Computer system and data classification method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |