CN112163798A - Big data driven self-regulating automobile processing assembly line digital twin system - Google Patents
Big data driven self-regulating automobile processing assembly line digital twin system Download PDFInfo
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Abstract
The invention discloses a big data driven self-regulating automobile processing assembly line digital twinning system. According to the technical scheme provided by the embodiment of the application, the real-time vehicle fault information is fed back to the digital twin region of the system in society, the digital twin region is used for self-analyzing and screening out the process problems which can be avoided during processing and assembling, and the self-regulation and control of the physical production line are realized according to an expert system, so that the processing production line is continuously perfected.
Description
The technical field is as follows:
the embodiment of the application relates to the field of automobile processing pipelines, in particular to a big data driven self-regulating automobile processing pipeline digital twin system.
Background art:
currently, the traditional processing method and means are gradually replaced by emerging technologies such as internet of things, big data, artificial intelligence and the like, and the digital twin technology serving as a digital mapping system of a real physical environment can serve the physical environment to a great extent, so that the physical environment becomes more intelligent, digital and controllable. The main line of "china manufacturing 2025" is digital network-based intelligent manufacturing which embodies deep integration of information technology and manufacturing technology. The method mainly comprises eight strategy strategies: digital network intelligent manufacturing is carried out; the design capability of the product is improved; the technical innovation system of the manufacturing industry is perfected; reinforcing the manufacturing foundation; the product quality is improved; green manufacturing is carried out; the method comprises the following steps of culturing enterprise groups and dominant industries with global competitiveness: the modern manufacturing service industry is developed. The big data driven self-regulating automobile processing assembly line digital twin system can realize the industry upgrade of the traditional field of automobile processing based on the big data and digital twin technology, and realize the green manufacture and intelligent manufacture of the automobile processing industry.
At present, a digital twin system only displays a three-dimensional model and cannot control a physical space according to a predicted condition.
The invention content is as follows:
the embodiment of the application provides a big data driven self-regulating automobile processing assembly line digital twin system, so that when a virtual processing scene is constructed and displayed, an actual processing assembly line process can be improved after screening according to the real-time fault condition of an automobile which is processed by the assembly line and is put into use, and self-collection of the real-time fault condition of the automobile, self-screening of fault information and self-improvement of the actual assembly line processing process are realized. The digital twinning system comprises a self-collection module, a self-screening module and a self-improvement module. The self-collection module comprises an information acquisition system and an information preliminary processing system, wherein the information acquisition system collects information from a real network environment. The self-screening module comprises an information induction system and an information screening system based on a pulse neural network. The self-improvement module comprises a processing technology digital twin body used for connecting a physical space and controlling the physical space.
The technical scheme is that a big data driven self-regulating automobile processing assembly line digital twin system comprises:
the system comprises a self-collection module and an information primary processing system, wherein the self-collection module comprises an information acquisition system and an information primary processing system, the information acquisition system is used for acquiring real-time traffic accident information from a public security system, a network news system and car repair factory maintenance data, and then the information primary processing system is used for deleting useless information by utilizing keyword query so as to perform primary information processing.
The self-screening module comprises an information induction system and an information screening system based on the impulse neural network, the information induction system induces information of data processed by the information primary processing system according to whether casualties exist or not and then transmits the information to the information screening system, and the information screening system screens and grades the data for the automobile damage grade according to the characteristic value based on the impulse neural network.
And the self-improvement module comprises a processing technology digital twin body used for connecting the physical space and controlling the physical space, the processing technology digital twin body performs data visualization on the classified data, establishes a simulation model, and connects and controls the processing technology of the processing workshop through a network protocol.
Drawings
These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:
FIG. 1 is a flow chart of a large data driven self-regulating automotive process line digital twinning system.
FIG. 2 is a flow chart of a self-collection module in a big data driven self-regulated automobile processing line digital twin system.
FIG. 3 is a flow chart of a self-screening module in a digital twin system of a big data driven self-regulated automotive process line.
FIG. 4 is a flow diagram of a self-modifying module in a large data driven self-regulated automotive process line digital twinning system.
101, a digital twinning system of a self-regulating automobile processing production line; 102 a self-collection module; 103 a self-screening module; 104 a self-improvement module; 201 an information acquisition system; 202 an information primary processing system; 301 information induction system; 302 an information screening system; 401 process figure twins.
Detailed Description
Embodiments of the present disclosure will be described below with reference to the accompanying drawings, where well-known functions or constructions are not described in detail in the following description to avoid obscuring the present disclosure in unnecessary detail.
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs, and the terms "first," "second," and the like, as used herein do not denote any order, quantity, or importance, but rather are used to distinguish one element from another. Furthermore, the terms "a" and "an" do not denote a quantity, but rather denote the presence of at least one of the referenced item. The terms "or" are inclusive and mean any or all of the listed objects, the present specification uses "including," "comprising," or "having" and variations thereof to mean including the objects listed thereafter and equivalents thereof as well as other objects, the terms "connected" and "coupled" are not limited to physical or mechanical connections or couplings, and can include direct or indirect electrical connections or couplings, and furthermore, terms indicating specific positions such as "top," "bottom," "left," and "right" are described with reference to specific drawings, embodiments disclosed in the present disclosure can be arranged in a manner different from that shown in the drawings, and thus, positional terms used in the present specification should not be limited to the positions described in the specific embodiments.
The digital contracture system described in this specification is a high fidelity digital replica or dynamic model of an asset or process that is used to continuously collect data, enhance insight, and simulate a human being to judge and control a pipeline to help people optimize a process from traffic accidents, digital contractures that use data from networks and databases to represent near real-time status and operating conditions of the asset or process have three advantages, e.g., first, the digital contracture has self-learning capabilities and can be continuously learned from new data to improve the process, and second, the digital contracture can be scalable, thus being able to run millions of contractures. Third, the digital contracture may adapt to other parts or process categories, new scenarios or factors.
The present disclosure may apply digital contractures to process planning management of physical systems. Further, in the digital twinning system and method of the present disclosure, the digital contracture body is used not only during operation of the physical system, but also during commissioning of the physical system. A big data driven self-regulated automotive process line digital twinning system of the present disclosure will be described in detail hereinafter with reference to the accompanying drawings.
A big data driven self-regulating automobile processing assembly line digital twin system.
Fig. 1 is a flow chart of a big data driven self-regulated automobile processing line digital twin system, as shown in fig. 1, a self-regulated automobile processing line digital twin system 101 includes a self-collection module 102, a self-screening module 103 and a self-improvement module 104, the self-collection module 102 deletes useless information from data Dai of a public security system, a network news system and a car repair factory for repairing an automobile by using keyword query to perform preliminary information processing and outputs data Da, the self-screening module 103 receives the data Da through a network protocol, such as a TCP/IP protocol, and summarizes the information into data Dbi according to whether there is casualty or not, and then screens and classifies the data into data Db for an automobile damage level according to a characteristic value based on a pulse neural network, the self-improvement module 104 through a network protocol, such as a TCP/IP protocol, and receiving the Data Db, visualizing the Data Db in unity3D, and connecting and controlling the processing technology of the processing workshop through a network protocol such as High-Level Data Link Control.
Fig. 2 is a flow chart of a self-collection module in a digital twin system of a big data-driven self-regulated automobile processing line, as shown in fig. 2, the self-collection module 102 includes an information acquisition system 201 for collecting information from a real network environment, and an information primary processing system 202, the information acquisition system acquires real-time traffic accident information from a public security system, a network news system, and data Dai of a car repair factory for repairing a car, and the information primary processing system deletes unnecessary information by using keyword query to perform primary information processing and outputs data Da.
Fig. 3 is a flow chart of a self-screening module in a digital twin system of a big data driven self-regulated automobile processing line, as shown in fig. 3, the self-screening module 103 includes an information induction system 301 and an information screening system 302 based on a pulse neural network, the information induction system induces information Da processed by an information primary processing system according to whether casualties exist, outputs data Dbi, and then transmits the data to the information screening system, and the information screening system screens and grades the data for automobile damage grades according to characteristic values based on the pulse neural network, and outputs data Db.
Fig. 4 is a flow chart of a self-improvement module in a digital twin system of a big Data driven self-regulating automobile processing line, as shown in fig. 4, the self-improvement module 104 includes a processing technology digital twin body 401 for connecting a physical space and controlling the physical space, the processing technology digital twin body 401 visualizes the graded Data Db in unity3D, and connects and controls the processing technology of the processing workshop through a network protocol High-Level Data Link Control.
While the disclosure has been illustrated and described in typical embodiments, it is not intended to be limited to the details shown, since various modifications and substitutions can be made without departing in any way from the spirit of the present disclosure, and further modifications and equivalents of the disclosure disclosed in this specification can be made by those skilled in the art using no more than routine experimentation, and all such modifications and equivalents are considered to be within the spirit and scope of the disclosure as defined in the following claims.
Claims (4)
1. A digital twinning system driven by big data and used for a self-regulating automobile processing assembly line comprises a self-collecting module, a self-screening module and a self-improving module. The self-collection module comprises an information acquisition system and an information preliminary processing system, wherein the information acquisition system collects information from a real network environment. The self-screening module comprises an information induction system and an information screening system based on a pulse neural network. The self-improvement module comprises a processing technology digital twin body used for connecting a physical space and controlling the physical space.
2. The self-collection module according to claim 1 comprises an information acquisition system for collecting information from a real network environment, an information primary processing system for acquiring real-time traffic accident information from a public security system, a network news system and car repair factory maintenance data, and then the information primary processing system performs primary information processing by deleting useless information by using keyword query.
3. The self-screening module of claim 1 comprises an information induction system and an information screening system based on the impulse neural network, wherein the information induction system induces information of data processed by the information primary processing system according to whether casualties exist or not and then transmits the information to the information screening system, and the information screening system screens and grades the data for the automobile damage grade according to the characteristic value based on the impulse neural network.
4. The self-improvement module of claim 1 comprises a processing technology digital twin body for connecting a physical space and controlling the physical space, wherein the processing technology digital twin body performs data visualization on the classified data, establishes a simulation model, and connects and controls a processing technology of a processing workshop through a network protocol.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117270482A (en) * | 2023-11-22 | 2023-12-22 | 博世汽车部件(苏州)有限公司 | Automobile factory control system based on digital twin |
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