CN113325816A - Industrial Internet-oriented digital twin body data management method - Google Patents
Industrial Internet-oriented digital twin body data management method Download PDFInfo
- Publication number
- CN113325816A CN113325816A CN202110883571.4A CN202110883571A CN113325816A CN 113325816 A CN113325816 A CN 113325816A CN 202110883571 A CN202110883571 A CN 202110883571A CN 113325816 A CN113325816 A CN 113325816A
- Authority
- CN
- China
- Prior art keywords
- data
- digital twin
- module
- industrial internet
- management method
- 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 33
- 238000013523 data management Methods 0.000 title claims abstract description 17
- 230000003993 interaction Effects 0.000 claims abstract description 21
- 239000000047 product Substances 0.000 claims abstract description 20
- 238000012800 visualization Methods 0.000 claims abstract description 19
- 238000004088 simulation Methods 0.000 claims abstract description 17
- 238000013500 data storage Methods 0.000 claims abstract description 7
- 239000012467 final product Substances 0.000 claims abstract description 7
- 238000007781 pre-processing Methods 0.000 claims abstract description 7
- 238000004458 analytical method Methods 0.000 claims abstract description 6
- 238000004806 packaging method and process Methods 0.000 claims abstract description 4
- 238000005516 engineering process Methods 0.000 claims description 33
- 238000004422 calculation algorithm Methods 0.000 claims description 16
- 238000004519 manufacturing process Methods 0.000 claims description 12
- 238000013473 artificial intelligence Methods 0.000 claims description 9
- 230000006870 function Effects 0.000 claims description 7
- 230000007246 mechanism Effects 0.000 claims description 6
- 238000013135 deep learning Methods 0.000 claims description 5
- 230000006854 communication Effects 0.000 claims description 4
- 238000004891 communication Methods 0.000 claims description 4
- 238000013527 convolutional neural network Methods 0.000 claims description 4
- 238000013461 design Methods 0.000 claims description 3
- 238000001514 detection method Methods 0.000 claims description 3
- 238000010801 machine learning Methods 0.000 claims description 3
- 238000007726 management method Methods 0.000 claims description 3
- 230000001360 synchronised effect Effects 0.000 claims description 3
- 230000015572 biosynthetic process Effects 0.000 claims description 2
- 238000012423 maintenance Methods 0.000 claims description 2
- 238000013439 planning Methods 0.000 claims description 2
- 230000008569 process Effects 0.000 description 13
- 238000012545 processing Methods 0.000 description 7
- 238000005457 optimization Methods 0.000 description 5
- 238000011161 development Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 3
- 238000013528 artificial neural network Methods 0.000 description 2
- 230000006399 behavior Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000010276 construction Methods 0.000 description 2
- 238000012937 correction Methods 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000002452 interceptive effect Effects 0.000 description 2
- 238000013507 mapping Methods 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 238000007619 statistical method Methods 0.000 description 2
- 238000012795 verification Methods 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000002776 aggregation Effects 0.000 description 1
- 238000004220 aggregation Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007175 bidirectional communication Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000000284 extract Substances 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012067 mathematical method Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012544 monitoring process Methods 0.000 description 1
- 230000006855 networking Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 238000007639 printing Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 238000009877 rendering Methods 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS] or computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/32—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials
- H04L9/3236—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions
- H04L9/3239—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols including means for verifying the identity or authority of a user of the system or for message authentication, e.g. authorization, entity authentication, data integrity or data verification, non-repudiation, key authentication or verification of credentials using cryptographic hash functions involving non-keyed hash functions, e.g. modification detection codes [MDCs], MD5, SHA or RIPEMD
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
- G05B2219/32252—Scheduling production, machining, job shop
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L9/00—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols
- H04L9/50—Cryptographic mechanisms or cryptographic arrangements for secret or secure communications; Network security protocols using hash chains, e.g. blockchains or hash trees
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Computer Security & Cryptography (AREA)
- General Engineering & Computer Science (AREA)
- Manufacturing & Machinery (AREA)
- Quality & Reliability (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Automation & Control Theory (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the technical field of industrial Internet, and realizes a digital twin body data management method facing the industrial Internet, which comprises the following steps: s1: data acquisition, namely acquiring product data and related data through a data acquisition module, and performing preprocessing and preliminary analysis; s2: constructing a digital twin body, namely establishing a model of the physical entity according to data through a digital twin module, and constructing a corresponding digital twin body; s3: data storage, namely packaging and storing the data of the digital twin module through a block chain module; s4: modeling prediction, namely performing data simulation and prediction through a data interaction module to realize the connection among a physical entity, a model and a digital twin; s5: the terminal display is carried out, and the virtual simulation result and the final product are displayed in a multi-dimension mode on the terminal through a visualization module; the data acquisition module is connected with the digital twin module, and the digital twin module is connected with the block chain module, the data interaction module and the visualization module.
Description
Technical Field
The invention relates to a digital twin body data management method, in particular to an industrial internet-oriented digital twin body data management method, and relates to the technical field of industrial internet.
Background
The essence and core of the industrial internet is that the equipment, production lines, factories, suppliers, products and customers are tightly connected and converged through an industrial internet platform. In recent years, a digital twin technology is widely applied to an industrial internet cloud platform, and the digital twin technology is used for simulating a physical entity, a process or a system in an information platform to realize interconnection and intercommunication of a physical domain and a virtual domain. The construction of a digital twin body needs a large amount of data calculation and involves huge data amount, so whether the shared data is safe and reliable is the premise and the basis of the rapid development of the industrial internet, and how to ensure the safety and reliability of the shared data becomes a huge challenge for the digital twin technology in the industrial internet.
Disclosure of Invention
The purpose of the invention is as follows: in order to overcome the defects in the prior art, the invention provides a digital twin data management method for an industrial internet.
The technical scheme is as follows: in order to achieve the above object, the present invention provides a digital twin data management method for an industrial internet, the method including the steps of: s1: data acquisition, namely acquiring product data and related data through a data acquisition module, and performing preprocessing and preliminary analysis; the data acquisition module comprises an intelligent sensing node and a data processor; the intelligent sensing node acquires equipment and product data and other associated data, and can realize autonomous acquisition, automatic synchronization and remote control of the data; the data processor is arranged at the data acquisition terminal and used for preprocessing and analyzing the intelligent sensing node data through a data detection algorithm of a convolutional neural network; the data of the intelligent sensing node is wirelessly transmitted in a 5G, WiFi or Bluetooth wireless high-speed network communication mode; s2: establishing a digital twin body, namely establishing a physical entity model according to the data transmitted by the data acquisition module through a digital twin module, and establishing a corresponding digital twin body; s3: data storage, namely packaging the data of the digital twin module into transactions through a block chain module and storing the data; s4: modeling prediction, namely performing data simulation and prediction through a data interaction module to realize the connection among a physical entity, a model and a digital twin; s5: the terminal display is carried out, and the virtual simulation result and the final product are displayed in a multi-dimension mode on the terminal through a visualization module; the data acquisition module is connected with the digital twin module, and the digital twin module is connected with the block chain module, the data interaction module and the visualization module. Furthermore, the digital twin module mainly comprises a physical space and a digital virtual space; the physical space is used for analyzing and serving twin data based on cloud computing, artificial intelligence and big data technology; the virtual space is used for carrying out multi-level and full-dimensional simulation and modeling on basic equipment, a product system and a production environment; the digital twin module maps data in a physical space in a digital virtual space, establishes a model and constructs a corresponding digital twin body, and the digital twin body realizes various functions of intelligent planning design, production management, operation maintenance and fault prediction of products.
Furthermore, the blockchain module builds a peer-to-peer network platform by relying on the internet technology; building blocks in the block chain module, wherein the blocks comprise block heads and block bodies, the block heads are unique identifications of the blocks, the block bodies are used for storing digital twin body data transmitted from the twin body modules, different blocks are chained together through a consensus mechanism to form a complete data block chain of the digital twin body, and the consensus mechanism prevents malicious nodes from damaging the formation of the blocks.
Furthermore, the block chain module packages data into transactions and stores the transactions, the structure of the stored data is a Merckel tree structure, data at different moments are collected within a preset time and form a new transaction, and the new transaction is stored in the form of the Merckel tree.
Furthermore, the data interaction module realizes the connection among the physical entity, the model and the digital twin body through data driving, so that information and data are mutually coupled, and data communication among the three is realized; and the interaction and synchronous feedback of the physical layer and the virtual digital twin layer are realized by utilizing an artificial intelligence algorithm of machine learning and deep learning, and the flow in a physical space is perfected.
Furthermore, the visualization module displays the virtual simulation result and the final product in a terminal in a multi-dimensional way through a visualization tool; the visualization module supports high-resolution three-dimensional visualization, and achieves the functions of achievement and model display, man-machine interaction and team collaborative work.
The invention has the beneficial effects that: the industrial internet technology is combined with the digital twin technology, and the life cycle process of equipment production is accurately simulated, predicted and analyzed through the real-time data intercommunication between the digital twin and the real equipment, so that the production and management efficiency is greatly improved. Meanwhile, the block chain technology is used for solving the safety and credibility problems of data in the digital twin construction process, greatly promotes the technological and intelligent properties of the industrial internet field, and can effectively solve the defects and shortcomings in the prior art.
Drawings
FIG. 1 is a structure diagram of a digital twin body data management method facing to an industrial internet;
FIG. 2 is a flow chart of a digital twin data management method facing an industrial Internet;
FIG. 3 is a block chain data storage diagram;
FIG. 4 is a diagram of a virtual twin model.
Detailed Description
The principles and features of this invention are described below in conjunction with examples which are set forth to illustrate, but are not to be construed to limit the scope of the invention.
Example one
An industrial internet-oriented digital twin data management method, comprising the following steps: s1: data acquisition, namely acquiring product data and related data through a data acquisition module, and performing preprocessing and preliminary analysis; s2: establishing a digital twin body, namely establishing a physical entity model according to the data transmitted by the data acquisition module through a digital twin module, and establishing a corresponding digital twin body; s3: data storage, namely packaging the data of the digital twin module into transactions through a block chain module and storing the data; s4: modeling prediction, namely performing data simulation and prediction through a data interaction module to realize the connection among a physical entity, a model and a digital twin; s5: the terminal display is carried out, and the virtual simulation result and the final product are displayed in a multi-dimension mode on the terminal through a visualization module; the data acquisition module is connected with the digital twin module, and the digital twin module is connected with the block chain module, the data interaction module and the visualization module.
The data acquisition module comprises an intelligent sensing node and a data processor; the data acquisition module is mainly used for acquiring product data and related data in real time through the intelligent sensing node. The data processor is mainly used for preprocessing the acquired data and carrying out primary analysis by a data detection algorithm of a convolutional neural network. The algorithm comprises the steps of firstly, generating about 2000 candidate areas for image data by adopting a selective search method, then, carrying out feature extraction on the candidate areas by using a convolutional neural network, then, classifying the extracted features by using an SVM classifier, finally, carrying out regression correction on the candidate areas by using a Bounding Box (Bounding Box), and generating coordinates of a prediction Box.
The digital twin module mainly comprises a physical space and a digital virtual space; the digital twin module establishes a digital twin body for a physical entity in reality and solves the actual problem according to an analog simulation, space mapping and prediction scheme. The method has the main effects of analyzing and serving twin data based on cloud computing, artificial intelligence and big data technology. And (3) carrying out multi-level and full-dimensional simulation and modeling on basic equipment, a product system and a production environment by combining a digitization technology and an artificial intelligence technology. Such as: using data-driven modeling to describe the characteristics of the system using mathematical and statistical methods, relationships to the input and output data; the optimal practice of the digital twin technology is guaranteed in an uncertain environment by using an artificial intelligence technology to efficiently sense the environment, analyze the current situation and combine a target to make an optimal decision.
The blockchain module guarantees the data security in the digital twin creation process and the virtual simulation process by utilizing the traceability, the security and the high availability of the blockchain technology. The block chain module builds a peer-to-peer network platform by relying on the Internet technology. And constructing a block in the block chain module, wherein the block comprises a block head and a block body, the block head is the unique identification of the block, and the block body is used for storing the data of the digital twin body obtained from the twin body module. And selecting a consensus node to participate in consensus through a consensus mechanism, finally generating a new block and linking different blocks together to form a complete data block chain of the digital twin.
The block chain module packages data into transactions and stores the transactions, the structure of the stored data is a Merckel tree structure, data at different moments are collected within preset time to form a new transaction, and the new transaction is stored in the form of the Merckel tree.
The data interaction module realizes interaction and synchronous feedback of a physical layer and a virtual digital twin layer by using an artificial intelligence algorithm of machine learning and deep learning, and perfects the flow in a physical space. The twin data is originated from the application services of the physical entity, the virtual model and the virtual twin, and the data interaction module connects the physical entity, the digital twin model and the digital twin into an organic whole, so that information and data are mutually coupled and interactively fed back among all parts, and bidirectional communication and service interaction are realized.
And the visualization module displays the virtual simulation result and the final product in a terminal in a multidimensional and more intuitive way through a visualization tool. High-resolution three-dimensional visualization is supported, and the application function requirements of achievement and model display, man-machine interaction and team cooperative work are met. The invention adopts a mode of combining Echarts and three. Real-time data or statistical data in the data simulation and modeling process is displayed by using the Echarts technology, the three.js technology provides rich three-dimensional model display functions, and the technology is used for realizing the visual display of a digital twin model, so that research personnel can monitor the dynamic change of simulation data in real time in a more intuitive and convenient interactive mode.
Example two
With the development of the internet and artificial intelligence, unmanned automobile technology has been proposed and gradually manufactured unmanned automobiles. However, many problems need to be solved in the process, such as: technical problems, safety problems, regulatory problems, etc. At present, the society enters the industrial 4.0 era, and a digital twin technology can be used for monitoring a physical process by combining an internet system in the manufacturing process of an automobile product, and a physical virtual copy is created and decision is made; and data storage in the process of maintaining is realized by using a block chain technology, so that the data security in the process is protected.
To facilitate understanding of the present embodiment, as shown in fig. 2, a flowchart of a digital twin data management method for an industrial internet according to the present invention includes the following steps:
according to the first step in the flow chart, an intelligent sensing chip is arranged on the automobile, data are collected in real time through an intelligent sensing node, and various data information of the produced automobile products is obtained. The traditional MCU chip can realize low-level assistant driving, but with the coming of high-level intelligent driving, the intelligent automobile faces the challenges of multidimensional perception requirements and massive unstructured data processing requirements. In order to improve the challenges of high data volume and high computational power in intelligent driving, the invention adopts an AI intelligent chip as a data processing and operation core of an intelligent automobile. For example: the neural network algorithm is integrated into a chip, so that the problems of high data processing and computing power can be solved, and the advantages of low cost and low power consumption are achieved. In the aspect of unmanned technology, not only independent twin data of an automobile is needed, but also relevant data such as weather, road conditions, real-time dynamics and the like are needed. The AI intelligent chip of the automobile is used for acquiring key data of each part of the automobile and road condition, positioning, weather and other data of a road, and a data processor in the module is used for carrying out preliminary processing on the acquired data and aggregation analysis on the data. Finally, the various data are aggregated to form a data source.
According to the second step in the flow chart, the data acquired by the data acquisition module establishes a digital twin with the physical entity in reality through the twin module. The invention adopts the deep learning technology to analyze and process the preprocessed data. For the production elements of the physical entity in the embodiment, the invention constructs the twin model from multiple levels of geometry, physics, behavior and rule, and really restores the running state of the physical entity. Firstly, a geometric model file is obtained through various ways provided by a manufacturer, and operations such as hole elimination are carried out by utilizing Pixyz studio to realize light processing, so that a primary geometric model is obtained. And then, constructing a digital twin virtual scene by using 3dsMax, and optimizing and rendering the model. And finally, constructing a physical model, a behavior model and a rule model of the digital twin plant through the Unity3 d.
Furthermore, in order to solve the problem that the accuracy requirements of different application scene models are different, the model is optimized by a three-dimensional model intelligent processing technology. Firstly, the automatic conversion of the model format is solved by identifying and analyzing semantic fields of the format in a common three-dimensional model. Then, by automatically identifying and segmenting the set structure and semantic features in common models such as a point cloud model, a grid model and the like, the structure of the model is intelligently processed, denoising optimization and feature extraction based on the semantic features are completed, and intelligent processing of the model structure is realized. And finally, establishing a digital twin body to complete the mapping from the physical space to the virtual space.
Furthermore, the data interaction module receives data from the physical entity to enable the digital model to evolve in real time, so as to realize virtual-real synchronization, namely real refers to the physical entity in reality, and virtual refers to the virtual digital model. The digital twin body is connected with the physical entity through the local network, reflects the real-time state and parameters of the physical entity, and extracts the state information of the data. Therefore, the life cycle of the automobile simulation system is consistent with the full life cycle of a physical entity, the connection between the automobile and a digital model can be finally realized, real-time interaction and feedback of data are carried out, and automobile products are simulated in an information platform. In particular, local networks include, but are not limited to, industrial field buses and wireless networks, such as: industrial ethernet, fiber optic network, or wifi.
During the development of the digital twin, historical data and historical virtual products are continually overlaid. Historical product data plays a very important role in the production design and technical correction of new products. The invention solves the problems of historical data storage and safety through a block chain technology.
Further, the core advantages of the blockchain technology are decentralization and security and credibility, data to be shared and stored are packaged through a specific hash algorithm and a Merkle data structure, networking is performed through a peer-to-peer network (P2P network), and then a block final blockchain is generated through a consensus protocol to complete storage of the data. The safety and credibility of historical product data are guaranteed through a block chain technology.
According to the third step in the flow chart, a peer-to-peer network (P2P network) platform is built by relying on the Internet technology. The block is constructed as shown in fig. 3, and includes a block header and a block body, wherein the block header is a unique identification of the block, and includes a Hash value, a time stamp, a root Hash value of a merkel number structure and a random number of a previous block, and the block body is used for storing data of a digital twin body obtained from the twin body module.
Further, the block chain module packages data into transactions and stores the transactions, the stored data structure is a Merckel tree structure, data at different moments are collected within preset time to form a new transaction, and the new transaction is stored in the form of the Merckel tree.
Further, the transaction information stored in the merkel tree is key data in the digital twin, all transactions are Hash-combined into a root Hash value, and the root Hash value is stored in the block as a unique summary of a group of transactions. The transaction is verified by using SPV (simple Payment verification), and the authenticity of the transaction is verified by calculating the Hash value of the transaction to be verified and comparing the Hash value of the root of the Mercker tree in the head of the local block and locating the block containing the transaction.
The invention is used for managing a large amount of data in a digital twin, so the invention classifies the data, and the data with the same type are stored in a Mercker tree structure. Furthermore, the blocks are verified according to the rights and interests identification mechanism, and the blocks after verification are connected according to the time sequence to construct a chain, so that a final block chain structure is formed.
According to the fourth step in the flow chart, simulation and prediction are carried out after data are received, an intelligent optimization algorithm is provided for simulation and prediction, the intelligent algorithm including but not limited to genetic algorithm, neural network, deep learning and other algorithms can also use the self-developed optimization algorithm and be embedded into the algorithm, and the optimization algorithm can be used for carrying out automatic optimization on large-scale parameters to quickly obtain optimized parameter configuration. The parameters are corrected through the real-time data feedback in the organic whole formed among the physical entity, the model and the digital twin body, so that the deviation in the simulation prediction process can be corrected quickly, and the final result is more accurate.
And according to the fifth step in the flow chart, generating response results of all the statistical analysis and simulation experiment data in the forms of tables, histograms, pie charts and the like, and forming report export printing. Meanwhile, real-time data or statistical data in the data simulation and modeling process are displayed by using the Echarts technology, the three.js technology provides rich three-dimensional model display functions, and the technology is used for realizing the visual display of a digital twin model, so that research personnel can monitor the dynamic change of simulation data in real time in a more intuitive and convenient interactive mode. Finally, the automobile is put into production according to the data result to form a real automobile product in reality.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (6)
1. A digital twin body data management method facing to an industrial internet is characterized by comprising the following steps: s1: data acquisition, namely acquiring product data and related data through a data acquisition module, and performing preprocessing and preliminary analysis; the data acquisition module comprises an intelligent sensing node and a data processor, wherein the intelligent sensing node acquires equipment and product data and other associated data and can realize autonomous acquisition, automatic synchronization and remote control of the data; the data processor is arranged at the data acquisition terminal and used for preprocessing and analyzing the intelligent sensing node data through a data detection algorithm of a convolutional neural network; the data of the intelligent sensing node is wirelessly transmitted in a 5G, WiFi or Bluetooth wireless high-speed network communication mode; s2: establishing a digital twin body, namely establishing a physical entity model according to the data transmitted by the data acquisition module through a digital twin module, and establishing a corresponding digital twin body; s3: data storage, namely packaging the data of the digital twin module into transactions through a block chain module and storing the data; s4: modeling prediction, namely performing data simulation and prediction through a data interaction module to realize the connection among a physical entity, a model and a digital twin; s5: the terminal display is carried out, and the virtual simulation result and the final product are displayed in a multi-dimension mode on the terminal through a visualization module; the data acquisition module is connected with the digital twin module, and the digital twin module is connected with the block chain module, the data interaction module and the visualization module.
2. The industrial internet-oriented digital twin data management method according to claim 1, characterized in that: the digital twin module mainly comprises a physical space and a digital virtual space; the physical space is used for analyzing and serving twin data based on cloud computing, artificial intelligence and big data technology; the virtual space is used for carrying out multi-level and full-dimensional simulation and modeling on basic equipment, a product system and a production environment; the digital twin module maps data in a physical space in a digital virtual space, establishes a model and constructs a corresponding digital twin body, and the digital twin body realizes various functions of intelligent planning design, production management, operation maintenance and fault prediction of products.
3. The industrial internet-oriented digital twin data management method according to claim 1, characterized in that: the block chain module builds a peer-to-peer network platform by relying on the Internet technology; building blocks in the block chain module, wherein the blocks comprise block heads and block bodies, the block heads are unique identifications of the blocks, the block bodies are used for storing digital twin body data transmitted from the twin body modules, different blocks are chained together through a consensus mechanism to form a complete data block chain of the digital twin body, and the consensus mechanism prevents malicious nodes from damaging the formation of the blocks.
4. The industrial internet-oriented digital twin data management method according to claim 1, characterized in that: the block chain module packages data into transactions and stores the transactions, the structure of the stored data is a Merckel tree structure, data at different moments are collected within preset time to form a new transaction, and the new transaction is stored in the form of the Merckel tree.
5. The industrial internet-oriented digital twin data management method according to claim 1, characterized in that: the data interaction module realizes the connection among the physical entity, the model and the digital twin body through data driving, so that information and data are mutually coupled, and the data communication among the three is realized; and the interaction and synchronous feedback of the physical layer and the virtual digital twin layer are realized by utilizing an artificial intelligence algorithm of machine learning and deep learning, and the flow in a physical space is perfected.
6. The industrial internet-oriented digital twin data management method according to claim 1, characterized in that: the visualization module displays the virtual simulation result and the final product in a terminal in a multi-dimensional way through a visualization tool; the visualization module supports high-resolution three-dimensional visualization, and achieves the functions of achievement and model display, man-machine interaction and team collaborative work.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110883571.4A CN113325816A (en) | 2021-08-03 | 2021-08-03 | Industrial Internet-oriented digital twin body data management method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110883571.4A CN113325816A (en) | 2021-08-03 | 2021-08-03 | Industrial Internet-oriented digital twin body data management method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113325816A true CN113325816A (en) | 2021-08-31 |
Family
ID=77426896
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110883571.4A Pending CN113325816A (en) | 2021-08-03 | 2021-08-03 | Industrial Internet-oriented digital twin body data management method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113325816A (en) |
Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113741766A (en) * | 2021-11-08 | 2021-12-03 | 山东捷瑞数字科技股份有限公司 | Visual acquisition tool for webpage codes |
| CN113792421A (en) * | 2021-09-04 | 2021-12-14 | 苏州特比姆智能科技有限公司 | TPM device management data processing system and method based on digital twin |
| CN114266864A (en) * | 2022-03-02 | 2022-04-01 | 杭州华鲤智能科技有限公司 | Method for storing true state change of real object |
| CN114513430A (en) * | 2022-01-14 | 2022-05-17 | 山东浪潮工业互联网产业股份有限公司 | State monitoring method and device for industrial Internet equipment and storage medium |
| CN114663074A (en) * | 2022-05-17 | 2022-06-24 | 腾云互联(浙江)科技有限公司 | Product data analysis method and system based on digital twins |
| CN114707043A (en) * | 2022-03-09 | 2022-07-05 | 北京量子伟业信息技术股份有限公司 | File management method and system based on meta-universe block chain technology |
| CN114943109A (en) * | 2022-05-24 | 2022-08-26 | 天津天河云筑工科技有限公司 | Engineering project digital delivery method and system based on BIM |
| CN115085947A (en) * | 2022-08-23 | 2022-09-20 | 深圳市迪博企业风险管理技术有限公司 | Data security inspection method and system in digital twin |
| CN115225662A (en) * | 2021-09-22 | 2022-10-21 | 北京邮电大学 | High-efficiency credible energy data processing method based on digital twin and block chain |
| CN115311027A (en) * | 2022-10-11 | 2022-11-08 | 工业云制造(四川)创新中心有限公司 | Supply chain management method and system based on digital twin |
| CN115495485A (en) * | 2022-09-30 | 2022-12-20 | 广西产研院人工智能与大数据应用研究所有限公司 | Internet of things application digital twinning method with block chain characteristic |
| CN115688491A (en) * | 2022-12-30 | 2023-02-03 | 长江水利委员会长江科学院 | Water conservancy digital twin simulation method based on block chain |
| CN117172641A (en) * | 2023-10-13 | 2023-12-05 | 暨南大学 | Production logistics management platform based on block chain and digital twin and implementation method |
| CN117634145A (en) * | 2023-10-30 | 2024-03-01 | 浙江工企信息技术股份有限公司 | Multi-dimensional simulation method and system based on digital twin |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109819233A (en) * | 2019-01-21 | 2019-05-28 | 哈工大机器人（合肥）国际创新研究院 | A kind of digital twinned system based on virtual image technology |
| US20200101601A1 (en) * | 2018-09-28 | 2020-04-02 | Siemens Aktiengesellschaft | Control system and method for a robot |
| CN111208759A (en) * | 2019-12-30 | 2020-05-29 | 中国矿业大学(北京) | Digital twin intelligent monitoring system for unmanned fully mechanized coal mining face of mine |
| CN111210359A (en) * | 2019-12-30 | 2020-05-29 | 中国矿业大学(北京) | Intelligent mine scene oriented digital twin evolution mechanism and method |
| CN111798068A (en) * | 2020-07-22 | 2020-10-20 | 南京纯白矩阵科技有限公司 | Digital twinning system based on block chain bottom layer |
| CN112818446A (en) * | 2021-01-26 | 2021-05-18 | 西安交通大学 | Construction method of intelligent workshop digital twin system |
-
2021
- 2021-08-03 CN CN202110883571.4A patent/CN113325816A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20200101601A1 (en) * | 2018-09-28 | 2020-04-02 | Siemens Aktiengesellschaft | Control system and method for a robot |
| CN109819233A (en) * | 2019-01-21 | 2019-05-28 | 哈工大机器人（合肥）国际创新研究院 | A kind of digital twinned system based on virtual image technology |
| CN111208759A (en) * | 2019-12-30 | 2020-05-29 | 中国矿业大学(北京) | Digital twin intelligent monitoring system for unmanned fully mechanized coal mining face of mine |
| CN111210359A (en) * | 2019-12-30 | 2020-05-29 | 中国矿业大学(北京) | Intelligent mine scene oriented digital twin evolution mechanism and method |
| CN111798068A (en) * | 2020-07-22 | 2020-10-20 | 南京纯白矩阵科技有限公司 | Digital twinning system based on block chain bottom layer |
| CN112818446A (en) * | 2021-01-26 | 2021-05-18 | 西安交通大学 | Construction method of intelligent workshop digital twin system |
Cited By (19)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113792421A (en) * | 2021-09-04 | 2021-12-14 | 苏州特比姆智能科技有限公司 | TPM device management data processing system and method based on digital twin |
| CN113792421B (en) * | 2021-09-04 | 2023-10-17 | 苏州特比姆智能科技有限公司 | TPM equipment management data processing system and method based on digital twinning |
| CN115225662A (en) * | 2021-09-22 | 2022-10-21 | 北京邮电大学 | High-efficiency credible energy data processing method based on digital twin and block chain |
| CN115225662B (en) * | 2021-09-22 | 2023-09-19 | 北京邮电大学 | Efficient trusted energy data processing method based on digital twin and blockchain |
| CN113741766A (en) * | 2021-11-08 | 2021-12-03 | 山东捷瑞数字科技股份有限公司 | Visual acquisition tool for webpage codes |
| CN114513430A (en) * | 2022-01-14 | 2022-05-17 | 山东浪潮工业互联网产业股份有限公司 | State monitoring method and device for industrial Internet equipment and storage medium |
| CN114513430B (en) * | 2022-01-14 | 2024-03-22 | 浪潮工业互联网股份有限公司 | State monitoring method, equipment and storage medium of industrial Internet equipment |
| CN114266864B (en) * | 2022-03-02 | 2022-07-29 | 杭州华鲤智能科技有限公司 | Method for storing true state change of real object |
| CN114266864A (en) * | 2022-03-02 | 2022-04-01 | 杭州华鲤智能科技有限公司 | Method for storing true state change of real object |
| CN114707043B (en) * | 2022-03-09 | 2022-10-21 | 北京量子伟业信息技术股份有限公司 | File management method and system based on meta-universe block chain technology |
| CN114707043A (en) * | 2022-03-09 | 2022-07-05 | 北京量子伟业信息技术股份有限公司 | File management method and system based on meta-universe block chain technology |
| CN114663074A (en) * | 2022-05-17 | 2022-06-24 | 腾云互联(浙江)科技有限公司 | Product data analysis method and system based on digital twins |
| CN114943109A (en) * | 2022-05-24 | 2022-08-26 | 天津天河云筑工科技有限公司 | Engineering project digital delivery method and system based on BIM |
| CN115085947A (en) * | 2022-08-23 | 2022-09-20 | 深圳市迪博企业风险管理技术有限公司 | Data security inspection method and system in digital twin |
| CN115495485A (en) * | 2022-09-30 | 2022-12-20 | 广西产研院人工智能与大数据应用研究所有限公司 | Internet of things application digital twinning method with block chain characteristic |
| CN115311027A (en) * | 2022-10-11 | 2022-11-08 | 工业云制造(四川)创新中心有限公司 | Supply chain management method and system based on digital twin |
| CN115688491A (en) * | 2022-12-30 | 2023-02-03 | 长江水利委员会长江科学院 | Water conservancy digital twin simulation method based on block chain |
| CN117172641A (en) * | 2023-10-13 | 2023-12-05 | 暨南大学 | Production logistics management platform based on block chain and digital twin and implementation method |
| CN117634145A (en) * | 2023-10-30 | 2024-03-01 | 浙江工企信息技术股份有限公司 | Multi-dimensional simulation method and system based on digital twin |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113325816A (en) | Industrial Internet-oriented digital twin body data management method | |
| Liu et al. | A systematic review of digital twin about physical entities, virtual models, twin data, and applications | |
| CN107402976A (en) | Power grid multi-source data fusion method and system based on multi-element heterogeneous model | |
| CN107122985A (en) | A kind of agricultural-product supply-chain traceability system based on Internet of Things and block chain | |
| Wu et al. | Research on the virtual reality synchronization of workshop digital twin | |
| Deng et al. | Metaverse-driven remote management solution for scene-based energy storage power stations | |
| CN110390020A (en) | The modeling method of semantic gateway and semantic gateway | |
| CN114357694A (en) | Transformer substation digital twinning method and device based on large-scale point cloud | |
| CN104240461B (en) | A kind of Power System Intelligent alarm 3-D graphic Dynamic Display method | |
| CN115860529A (en) | Supply chain carbon accounting system based on industrial internet | |
| CN114218763A (en) | Production line dynamic virtual recombination method based on digital twin | |
| CN115423132A (en) | Engineering machinery predictive maintenance method based on digital twinning | |
| Guo et al. | Art product design and vr user experience based on iot technology and visualization system | |
| CN116384020A (en) | Digital twin substation space layout method integrating multidimensional semantic information | |
| CN117240887A (en) | Wisdom thing networking energy management platform system | |
| CN108647860A (en) | Power equipment state monitoring based on OpenStack and assessment cloud platform | |
| CN116414081A (en) | Intelligent workshop real-time monitoring method based on digital twinning | |
| Liu et al. | A dynamic updating method of digital twin knowledge model based on fused memorizing-forgetting model | |
| CN111651890B (en) | Data-driven aluminum electrolysis digital twin factory, control method and system | |
| CN112580957A (en) | Smart energy management and control system based on cloud platform | |
| Özturk | The Evolution of Building Information Model: Cognitive Technologies Integration for Digital Twin Procreation | |
| CN115457211A (en) | Transformer substation management method and system based on digital twins | |
| Li et al. | Research on digital twin and collaborative cloud and edge computing applied in operations and maintenance in wind turbines of wind power farm | |
| Shaikh et al. | Recent applications of computing and mobility technologies to modern manufacturing | |
| Deng | Digital Twin‐Based Modeling of Complex Systems for Smart Aging |
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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210831 |