CN112035905B - Self-learning three-dimensional modeling method and system - Google Patents
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Abstract
The invention provides a self-learning three-dimensional modeling method and system, and relates to the technical field of computer design. The three-dimensional modeling method comprises the following steps: collecting demand information input by a user on an interactive interface; matching the demand information with a plurality of design materials pre-stored in a database, selecting one or more design materials related to the demand information, and displaying the one or more design materials; monitoring and recording the modification process of the user to the one or more design materials in real time; and updating the one or more design data according to the modification process and parameterizing the one or more design data to construct a parameterized model. The three-dimensional modeling method provided by the invention can improve the design efficiency and the design quality.
Description
Technical Field
The invention relates to the technical field of computer design, in particular to a self-learning three-dimensional modeling method and system.
Background
In the long-term product development process, most enterprises form a series of mature design standards and Know-How (Know-How) and accumulate a large number of model databases. The application of the standard and model data can effectively help enterprises to improve the design efficiency and the generalization rate of products, reduce the development cost and improve the design quality of the products.
These design criteria and Know-How exist today mainly in the form of documents, such as design manuals, standard documents, libraries of problems, failure Modes and Effect Analysis (FMEA), etc. When a designer designs, a great deal of time is required to learn the standards and Know-How, even if the design product is still difficult to ensure to completely meet the requirements, especially for the designer with insufficient design experience, the importance of the standards and experience is easier to ignore, so that the designed product has defects, the project development period is influenced by the subsequent repeated design change, and the serious problem is caused by the fact that the defective product flows into the market to bring great potential safety hazard and economic loss.
In addition, in the field of industrial design, computer aided design (Computer Aided Design abbreviated as CAD) type design software is commonly used for completing the creation of the three-dimensional model. At present, all CAD software belongs to general design software and cannot carry out deep interaction with a large amount of historical model data accumulated by enterprises, although some large CAD software is provided with a knowledge engineering module, a user can build a parameterized model in advance through the knowledge engineering module, and in the subsequent design process, the part design can be rapidly completed by simply updating parameters through calling the parameterized model, and no redesign is needed. Some enterprises have accumulated a part of own parameterized model libraries, such as bolts, gaskets, and parts of enterprise standard components, for designers to call, improving a part of efficiency. But this is limited to the search, recall, and modification of a small number of simple part model data, which is still very difficult for designers if there are hundreds or thousands of complex part model data.
Disclosure of Invention
It is an object of a first aspect of the present invention to provide a self-learning three-dimensional modeling method capable of improving design efficiency and design quality.
It is a further object of the first aspect of the present invention to provide a three-dimensional modeling method that enables a design mode to be changed from a traditional stand-alone to a networking mode.
It is an object of a second aspect of the present invention to provide a self-learning three-dimensional modeling system capable of improving design efficiency and design quality.
According to the first aspect, the present invention provides a self-learning three-dimensional modeling method, including:
collecting demand information input by a user on an interactive interface;
matching the demand information with a plurality of design materials pre-stored in a database, selecting one or more design materials related to the demand information, and displaying the one or more design materials;
monitoring and recording the modification process of the user to the one or more design materials in real time;
and updating the one or more design data according to the modification process and parameterizing the one or more design data to construct a parameterized model.
Optionally, the real-time monitoring and recording of the user modification process to the one or more design materials further includes:
classifying the operation instructions involved in the modification process according to a fuzzy clustering algorithm.
Optionally, updating and parameterizing the one or more design data according to the modification process to form a parameterized model includes:
performing severity assessment on the classified operation instructions, and selecting effective characteristic data from the operation instructions according to assessment results;
training the effective characteristic data through a model to form standard model data;
and carrying out parameterization on the standard model data to construct a parameterized model.
Optionally, updating and parameterizing the one or more design data according to the modification process to form a parameterized model further includes:
performing severity assessment on the classified operation instructions based on the standard, know-how and model data in the database, and selecting effective characteristic data from the operation instructions according to assessment results;
training the effective characteristic data according to an expert model data framework formed based on standards, know-how and model data to form standard model data;
and carrying out parameterization construction on the standard model data by different software systems to form parameterized models meeting the format requirements of different software systems.
Optionally, before classifying the operation instruction related in the modification process according to the fuzzy clustering algorithm, the method further comprises:
and monitoring and recording the API and tool instructions in the software system called by the user and the historical model data, standards and know-how in the server in real time.
According to the second aspect, the present invention further provides a self-learning three-dimensional modeling system, including:
the acquisition unit is used for acquiring the requirement information input by the user on the interactive interface;
the matching unit is used for matching the demand information with a plurality of design materials pre-stored in a database, selecting one or more design materials related to the demand information and displaying the one or more design materials;
the monitoring unit is used for monitoring and recording the modification process of the user on the one or more design materials in real time;
and the self-learning unit is used for updating the one or more design data according to the modification process and constructing and parameterizing the one or more design data to form a parameterized model.
Optionally, the monitoring unit includes:
and the data preprocessing module is used for classifying the operation instructions related in the modification process according to the fuzzy clustering algorithm.
Optionally, the self-learning unit is further configured to:
performing severity assessment on the classified operation instructions, and selecting effective characteristic data from the operation instructions according to assessment results;
training the effective characteristic data through a model to form standard model data;
and carrying out parameterization on the standard model data to construct a parameterized model.
Optionally, the self-learning unit includes:
the feature screening module is used for carrying out severity assessment on the classified operation instructions based on the standard, know-how and model data in the database, and selecting effective feature data from the operation instructions according to an assessment result;
the model training module is used for training the effective characteristic data to form standard model data according to an expert model data frame formed based on standards, know-how and model data;
and the model construction module is used for carrying out parameterization construction on the standard model data by different software systems to form parameterized models meeting the requirements of different software system formats.
Optionally, the monitoring unit further includes:
and the data acquisition module is used for monitoring and recording the historical model data, standards and know-how called by the user and calling the API and tool instructions in the software system and the server in real time.
According to the three-dimensional modeling method provided by the invention, firstly, the demand information input by a user on an interactive interface is collected, the demand information is obtained according to the operation of the user on the interactive interface, a plurality of design materials are prestored in a database, the design materials are stored according to a set rule and algorithm, then the demand information is matched with the plurality of design materials, one or more design materials related to the demand information are selected according to the matching result, and the selected design materials are displayed for the user to use, and the user only needs to carry out secondary modeling and modification on the selected design materials without independent redesign. Further, monitoring and recording secondary modeling and modification of the user, updating the selected design data according to the modification process, and parameterizing the updated design data to construct a parameterized model for subsequent use by the user. Through repeated self-learning of the steps, the work of secondary modeling and modification is less and less for a user every time, the intelligent degree of the system is higher and higher, the maturity and the modularization degree of product design are higher and higher, the design efficiency and the design quality of the user are effectively improved, and the experience data of enterprises are well inherited.
Further, the user interacts with the information in the database through a unified interaction interface (which can be a web page or an app interface), a plurality of design materials stored in the database can be used by the user at any time, and the user performs secondary modeling and modification on the selected design materials and is stored in the database, so that the design mode is changed from a traditional single machine to a networking mode.
The above, as well as additional objectives, advantages, and features of the present invention will become apparent to those skilled in the art from the following detailed description of a specific embodiment of the present invention when read in conjunction with the accompanying drawings.
Drawings
Some specific embodiments of the invention will be described in detail hereinafter by way of example and not by way of limitation with reference to the accompanying drawings. The same reference numbers will be used throughout the drawings to refer to the same or like parts or portions. It will be appreciated by those skilled in the art that the drawings are not necessarily drawn to scale. In the accompanying drawings:
FIG. 1 is a flow diagram of a self-learning three-dimensional modeling method according to one embodiment of the invention;
FIG. 2 is a flow diagram of a self-learning three-dimensional modeling method according to another embodiment of the invention;
FIG. 3 is a block diagram of a self-learning three-dimensional modeling system according to one embodiment of the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by referring to the drawings are illustrative and intended to explain the present invention and should not be construed as limiting the invention.
The inventors have discovered during development that current enterprises use Product Data Management (PDM) systems to manage resources such as standards, know-How, product data models, etc. The system is also a system for integrating and uniformly managing information related to products and processes related to products, but the system is more focused on the full life cycle information management of product data. The system lacks self-learning function, and can not interact with the existing software (such as CAD: CATIA/NX/SolidWorks), so that the system has little help to the designer in the new product development modeling stage, and focuses on the later set-up change management and maintenance of the product data.
In order to solve the problems that in the traditional product design mode, standards formed by enterprises, precipitated Know-How and accumulated standard model data cannot be conveniently and quickly applied to the new product design process, the repeated modeling workload of users in the product design process is large, the design efficiency is low, the modularization and standardization degree of products are low, the design quality is uneven, and the like, the invention provides a self-learning three-dimensional modeling method, which has the greatest advantages that model data meeting the requirements of designers are automatically matched from a massive enterprise resource library according to the design requirements of the users, and meanwhile, the self-learning function is provided, so that the matching accuracy is continuously improved, and the design efficiency and the design quality are greatly improved.
FIG. 1 is a flow diagram of a self-learning three-dimensional modeling method according to one embodiment of the invention. As shown in fig. 1, the present invention provides a self-learning three-dimensional modeling method, which generally includes:
s10: collecting demand information input by a user on an interactive interface;
s20: matching the demand information with a plurality of design materials pre-stored in the database 51, selecting one or more design materials related to the demand information and displaying the one or more design materials;
s30: monitoring and recording the modification process of one or more design materials by a user in real time;
s40: and updating one or more design data according to the modification process and constructing parameterized models in a parameterized manner.
According to the three-dimensional modeling method provided by the embodiment, firstly, the requirement information input by a user on an interactive interface is collected, the requirement information is obtained according to the operation of the user on the interactive interface, a plurality of design materials are prestored in a database 51, the design materials are stored according to a set rule and algorithm, then the requirement information is matched with the plurality of design materials, one or more design materials related to the requirement information are selected according to the matching result, and the selected design materials are displayed for the user to use, and the user only needs to perform secondary modeling and modification on the selected design materials without independent redesign. Further, monitoring and recording secondary modeling and modification of the user, updating the selected design data according to the modification process, and parameterizing the updated design data to construct a parameterized model for subsequent use by the user. Through repeated self-learning of the steps, the work of secondary modeling and modification is less and less for a user every time, the intelligent degree of the system is higher and higher, the maturity and the modularization degree of product design are higher and higher, the design efficiency and the design quality of the user are effectively improved, and the experience data of enterprises are well inherited.
Further, the user interacts with the information in the database 51 through a unified interaction interface (may be a web page or app interface), the database 51 may be set in the server 50, and the server 50 may be a private cloud or a public cloud. The several design materials stored in the database 51 can be used by the user at any time, and the secondary modeling and modification of the selected design materials by the user are stored in the database 51, so that the design mode is changed from the traditional stand-alone mode to the networking mode.
In a preferred embodiment, the process of monitoring and recording in real time the user's modification of one or more design materials further comprises:
classifying the operation instructions involved in the modification process according to a fuzzy clustering algorithm.
FIG. 2 is a flow diagram of a self-learning three-dimensional modeling method according to another embodiment of the invention. In a more preferred embodiment, as shown in FIG. 2, updating and parameterizing one or more design materials according to a modification process to form a parameterized model comprises:
s41: performing severity assessment on the classified operation instructions, and selecting effective characteristic data from the operation instructions according to assessment results;
s42: training the effective characteristic data through a model to form standard model data;
s43: and carrying out parameterization on the standard model data to construct a parameterized model.
In a further embodiment, updating and parameterizing the one or more design data according to the modification process to form a parameterized model further comprises:
performing severity assessment on the classified operation instructions based on the standard, know-how and model data in the database 51, and selecting effective characteristic data from the operation instructions according to assessment results;
training the effective characteristic data according to an expert model data framework formed based on standards, know-how and model data to form standard model data;
and carrying out parameterization construction on the standard model data by different software systems to form parameterized models meeting the requirements of different software system formats.
The system is pre-stored with a plurality of data format standards and data interfaces corresponding to different software systems, so that the standard model data can be constructed in a parameterized mode according to the data format standards and the data interfaces corresponding to the different software systems, and the applicability of the standard model data is greatly expanded.
In a specific embodiment, before classifying the operation instructions involved in the modification process according to the fuzzy clustering algorithm, the method further comprises:
the user has invoked the APIs and tool instructions in the software system and the historical model data, standards and know-how in server 50 are monitored and recorded in real time.
FIG. 3 is a block diagram of a self-learning three-dimensional modeling system according to one embodiment of the invention. As shown in fig. 3, the present invention also provides a self-learning three-dimensional modeling system generally including an acquisition unit 10, a matching unit 20, a monitoring unit 30, and a self-learning unit 40. The collection unit 10 is used for collecting requirement information input by a user on the interactive interface. The matching unit 20 is configured to match the requirement information with a plurality of design data pre-stored in the database 51, and then select one or more design data related to the requirement information and display the one or more design data. The monitoring unit 30 is used for monitoring and recording the modification process of one or more design materials by a user in real time. The self-learning unit 40 is used for updating one or more design data according to the modification process and parameterizing the design data to form a parameterized model.
The three-dimensional modeling system provided in this embodiment includes a collection unit 10, a matching unit 20, a monitoring unit 30 and a self-learning unit 40, firstly, the collection unit 10 collects the requirement information input by the user on the interactive interface, obtains the requirement information according to the operation of the user on the interactive interface, prestores a plurality of design data in the database 51, stores the design data according to the established rule and algorithm, then the matching unit 20 matches the requirement information with the plurality of design data, selects one or more design data related to the requirement information according to the matching result and displays the selected design data for the user to use, and the user only needs to perform secondary modeling and modification on the selected design data without individual redesign. Further, the monitoring unit 30 monitors and records the secondary modeling and modification of the user in real time, updates the selected design data according to the modification process, and finally parameterizes the updated design data by the self-learning unit 40 to form a parameterized model for subsequent use by the user. Through repeated self-learning of the steps, the work of secondary modeling and modification is less and less for a user every time, the intelligent degree of the system is higher and higher, the maturity and the modularization degree of product design are higher and higher, the design efficiency and the design quality of the user are effectively improved, and the experience data of enterprises are well inherited.
Further, the user interacts with the information in the database 51 through a unified interaction interface (may be a web page or app interface), and the database 51 may be set in an enterprise server, where the enterprise server may be a private cloud or a public cloud. The several design materials stored in the database 51 can be used by the user at any time, and the secondary modeling and modification of the selected design materials by the user are stored in the database 51, so that the design mode is changed from the traditional stand-alone mode to the networking mode.
The three-dimensional modeling system CAN interact with CAD software in a user computer in real time through a network, one or more design data are automatically matched according to the requirement information according to the operation of the user in an interaction interface, and the design data are issued to the user computer and displayed in CAN software.
The three-dimensional modeling system is connected with a server 50, the server 50 can be a traditional hardware equipment server 50 or a cloud server 50, and the cloud server 50 can be a private cloud or a public cloud and is used for processing various requests and data transmission tasks of various interaction interfaces, databases 51, CAD systems, self-learning units 40 and monitoring units 30 in a network. The database 51 stores therein a plurality of design data formed by standard, know-How and model data accumulated by the enterprise through long-term practice and numerous projects, and various kinds of data information autonomously generated from the learning unit 40. The CAD system is mainly referred to herein as computer-aided design software such as CATIA/NX/SolidWorks, which is commonly used in the automotive industry, and makes an Application Program Interface (API) of the software directly interact with the server 50 through its secondary development technology, automatically execute various instructions received from the server 50, and transmit various information to the monitoring unit 30.
The main function of the self-learning unit 40 is to evaluate the severity of the classification data transmitted from the monitoring unit 30 based on the historical experience data such as standard, know-How and model data in the database 51, select valid feature data from the classification data according to the evaluation result, perform model training on the valid feature data to obtain standard model data, and parameterize the model after the standard model is obtained to form a parameterized model and transmit the parameterized model to the database 51 of the server 50.
In a preferred embodiment, the monitoring unit 30 comprises a data preprocessing module 31 for classifying the operation instructions involved in the modification process according to a fuzzy clustering algorithm. The monitoring unit 30 is used for completing real-time data acquisition in the three-dimensional model design process and carrying out clustering grouping pretreatment on acquired data information. The main function of the data preprocessing module 31 is to automatically classify the data acquired by the data acquisition module 32 according to the fuzzy clustering algorithm, and transmit the data to the self-learning unit 40.
In a more preferred embodiment, the self-learning unit 40 is further configured to evaluate the severity of the classified operation instruction, select valid feature data from the operation instruction according to the evaluation result, form standard model data by model training on the valid feature data, and parameterize the standard model data to form a parameterized model.
In a particular embodiment, the self-learning unit 40 includes a feature screening module 41, a model training module 42, and a model building module 43. The feature screening module 41 is configured to perform severity assessment on the classified operation instructions based on the criteria, know-how and model data in the database 51, and select valid feature data from the operation instructions according to the assessment result. Model training module 42 is configured to train the valid feature data into standard model data according to an expert model data framework formed based on the standards, know-how, and model data. The model construction module 43 is used for performing parameterization construction on the standard model data to form parameterized models meeting the requirements of different software system formats.
In a specific embodiment, the monitoring unit 30 further includes a data collection module 32 for monitoring and recording in real time historical model data, standards and know-how in the server 50, and the user has invoked the APIs and tool instructions in the software system. The primary purpose of the data acquisition module 32 is to monitor in real time the designer's operational instructions during the product design process, including but not limited to which APIs and tool instructions in the CAD software are called and which historical model data in the server 50 is called to see which criteria and Know-How, etc.
By now it should be appreciated by those skilled in the art that while a number of exemplary embodiments of the invention have been shown and described herein in detail, many other variations or modifications of the invention consistent with the principles of the invention may be directly ascertained or inferred from the present disclosure without departing from the spirit and scope of the invention. Accordingly, the scope of the present invention should be understood and deemed to cover all such other variations or modifications.
Claims (4)
1. A self-learning three-dimensional modeling method, comprising:
collecting demand information input by a user on an interactive interface;
matching the demand information with a plurality of design materials pre-stored in a database, selecting one or more design materials related to the demand information, and displaying the one or more design materials;
monitoring and recording the modification process of the user to the one or more design materials in real time;
updating and parameterizing the one or more design data according to the modification process to form a parameterized model;
the real-time monitoring and recording of the user modification process to the one or more design materials further comprises:
classifying the operation instructions involved in the modification process according to a fuzzy clustering algorithm;
updating and parameterizing the one or more design data according to the modification process to form a parameterized model further comprises:
performing severity assessment on the classified operation instructions based on the standard, know-how and model data in the database, and selecting effective characteristic data from the operation instructions according to assessment results;
training the effective characteristic data according to an expert model data framework formed based on standards, know-how and model data to form standard model data;
and carrying out parameterization construction on the standard model data by different software systems to form parameterized models meeting the format requirements of different software systems.
2. The three-dimensional modeling method of claim 1, further comprising, before classifying the operation instructions involved in the modification process according to a fuzzy clustering algorithm:
and monitoring and recording the API and tool instructions in the software system called by the user and the historical model data, standards and know-how in the server in real time.
3. A self-learning three-dimensional modeling system, comprising:
the acquisition unit is used for acquiring the requirement information input by the user on the interactive interface;
the matching unit is used for matching the demand information with a plurality of design materials pre-stored in a database, selecting one or more design materials related to the demand information and displaying the one or more design materials;
the monitoring unit is used for monitoring and recording the modification process of the user on the one or more design materials in real time;
the self-learning unit is used for updating the one or more design data according to the modification process and constructing a parameterized model in a parameterized manner;
the monitoring unit includes:
the data preprocessing module is used for classifying the operation instructions related in the modification process according to the fuzzy clustering algorithm;
the self-learning unit includes:
the feature screening module is used for carrying out severity assessment on the classified operation instructions based on the standard, know-how and model data in the database, and selecting effective feature data from the operation instructions according to an assessment result;
the model training module is used for training the effective characteristic data to form standard model data according to an expert model data frame formed based on standards, know-how and model data;
and the model construction module is used for carrying out parameterization construction on the standard model data by different software systems to form parameterized models meeting the requirements of different software system formats.
4. A three-dimensional modeling system in accordance with claim 3, wherein the monitoring unit further comprises:
and the data acquisition module is used for monitoring and recording the API and tool instructions in the software system called by the user and the historical model data, standards and know-how in the server in real time.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008287684A (en) * | 2007-05-16 | 2008-11-27 | Fusajiro Matsuura | Design creation supporting system utilizing the internet |
| CN103093045A (en) * | 2013-01-10 | 2013-05-08 | 浙江工业大学 | Interactive product configuration platform |
| CN103578137A (en) * | 2013-11-29 | 2014-02-12 | 中国建筑第八工程局有限公司 | Three-dimensional modeling system and method for prefabricated parts |
| CN103838916A (en) * | 2014-01-09 | 2014-06-04 | 华北电力大学 | Mechanical part quadratic parametric rapid modeling method |
| CN107256009A (en) * | 2017-06-30 | 2017-10-17 | 武汉理工大学 | A kind of Digital product model Intelligent assembly system based on deep learning |
| CN107886567A (en) * | 2017-12-08 | 2018-04-06 | 上海德稻集群文化创意产业（集团）有限公司 | A kind of three-dimensional quick scan matching identification and 3 D scanning system |
| CN110263376A (en) * | 2019-05-21 | 2019-09-20 | 西安理工大学 | A kind of beam bridge 3 D Parametric Modeling method |
| CN110942155A (en) * | 2019-11-29 | 2020-03-31 | 广西电网有限责任公司 | Research method of machine learning engine |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10977397B2 (en) * | 2017-03-10 | 2021-04-13 | Altair Engineering, Inc. | Optimization of prototype and machine design within a 3D fluid modeling environment |
| US20200103875A1 (en) * | 2018-09-28 | 2020-04-02 | Rockwell Automation Technologies, Inc. | Industrial automation project acceleration |
-
2020
- 2020-09-30 CN CN202011065815.XA patent/CN112035905B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2008287684A (en) * | 2007-05-16 | 2008-11-27 | Fusajiro Matsuura | Design creation supporting system utilizing the internet |
| CN103093045A (en) * | 2013-01-10 | 2013-05-08 | 浙江工业大学 | Interactive product configuration platform |
| CN103578137A (en) * | 2013-11-29 | 2014-02-12 | 中国建筑第八工程局有限公司 | Three-dimensional modeling system and method for prefabricated parts |
| CN103838916A (en) * | 2014-01-09 | 2014-06-04 | 华北电力大学 | Mechanical part quadratic parametric rapid modeling method |
| CN107256009A (en) * | 2017-06-30 | 2017-10-17 | 武汉理工大学 | A kind of Digital product model Intelligent assembly system based on deep learning |
| CN107886567A (en) * | 2017-12-08 | 2018-04-06 | 上海德稻集群文化创意产业（集团）有限公司 | A kind of three-dimensional quick scan matching identification and 3 D scanning system |
| CN110263376A (en) * | 2019-05-21 | 2019-09-20 | 西安理工大学 | A kind of beam bridge 3 D Parametric Modeling method |
| CN110942155A (en) * | 2019-11-29 | 2020-03-31 | 广西电网有限责任公司 | Research method of machine learning engine |
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