CN117633967A - Digital virtual factory construction system - Google Patents
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- CN117633967A CN117633967A CN202311558127.0A CN202311558127A CN117633967A CN 117633967 A CN117633967 A CN 117633967A CN 202311558127 A CN202311558127 A CN 202311558127A CN 117633967 A CN117633967 A CN 117633967A
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- 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
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Abstract
The invention discloses a digital virtual factory building system which comprises an acquisition module, a building module, a digital twin building module and a visual presentation module. The acquisition module is used for acquiring sensing data of the factory; the construction module is used for deploying an industrial big data platform, storing the sensing data in the factory on the industrial big data platform and sending the sensing data to the digital twin construction module; the digital twin construction module is used for constructing a digital twin model according to the sensing data; the visual presentation module is used for realizing the visualization of the digital twin model. According to the invention, the industrial big data platform is constructed, and the sensing data is stored on the industrial big data platform, so that not only can mass data be stored, but also the speed of receiving and transmitting the data can be improved, and the construction efficiency of the digital twin module can be improved.
Description
Technical Field
The invention belongs to the technical field of twinning, and particularly relates to a digital virtual factory building system.
Background
The digital twin is to fully utilize a physical model, integrate simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities and simulate the behavior of the physical model in a real environment. Along with the development of the traditional manufacturing industry to the intelligent manufacturing direction, the entity factory and the virtual factory are subjected to interactive control management, so that the upgrading and transformation of the industry are realized, and the working efficiency of the entity factory is improved.
Chinese patent application number CN201910253799 discloses a plant management system based on digital twin platform and method thereof, including a plant layer including a plant building digital twin in the 3D model and parameter information of the plant building digital twin; the workshop layer comprises a workshop equipment digital twin body in a workshop and parameter information of the workshop equipment digital twin body; the equipment layer comprises a production equipment digital twin body and parameter information of the production equipment digital twin body; and the control layer is used for controlling the digital twin bodies in the factory layer, the workshop layer and the equipment layer to generate corresponding actions according to signals transmitted in an actual workshop, and selecting and checking any digital twin body and parameter information thereof in the factory layer, the workshop layer and the equipment layer.
According to the scheme, basic information and running states of equipment in the workshop are displayed in real time through the establishment of the digital twin body and synchronous communication connection between the digital twin body and the actual workshop, and any equipment can be selected for monitoring according to requirements. Because mass data of actual workshops need to be stored in the process of establishing the digital twin body, the receiving and transmitting of the mass data can influence the speed of establishing the digital twin body, and further influence the efficiency of constructing the digital twin body.
In view of this, the present invention has been made.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, and provides a digital virtual factory building system which can not only store mass data, but also improve the speed of receiving and transmitting the mass data and the building efficiency of a digital twin module by building an industrial large data platform.
In order to solve the technical problems, the invention adopts the basic conception of the technical scheme that:
a digital virtual factory building system comprises an acquisition module, a building module, a digital twin building module and a visual presentation module;
the acquisition module is used for acquiring sensing data of the factory, wherein the sensing data at least comprises one or more of simulation data of products in the factory, process flow data of equipment in the factory, and motion data of a robot in the factory;
the construction module is used for deploying an industrial big data platform, storing the sensing data in the factory on the industrial big data platform and sending the sensing data to the digital twin construction module;
the digital twin construction module is used for constructing a digital twin model according to the sensing data;
the visual presentation module is used for realizing the visualization of the digital twin model.
Preferably, an industrial big data platform is deployed based on an HDFS system, an edge computing gateway is arranged, the collected sensing data is preprocessed, and the preprocessed data is stored on the industrial big data platform.
Preferably, the construction of the industrial big data platform based on the HDFS system specifically includes constructing an HDFS cluster, setting the number of servers of the HDFS cluster according to the data after preprocessing, storing the data after preprocessing in the servers of the HDFS cluster, and implementing interaction with the HDFS system by using an interface.
Preferably, constructing the industrial large data platform based on the HDFS system further includes integrating Spark components on the industrial large data platform for parallel analysis of the stored data.
Preferably, constructing a digital twin model from the sensing data includes constructing a process flow digital twin model from process flow data of the equipment within the plant; constructing a product digital twin model according to simulation data of products in a factory; and constructing a digital twin model of the robot according to the motion data and the motion data of the robot in the factory.
Preferably, a Process Mining algorithm is used to construct a Process flow digital twin model of the physical device.
Preferably, the Process flow data is imported in a Process Mining algorithm, a Process flow digital twin model is obtained through a Process Discovery algorithm, and the Process flow digital twin model is evaluated through a Conformance Checking algorithm.
Preferably, the design diagram of the product is collected, and a digital twin model of the product is generated by adopting a GANs implementation mode according to the design diagram of the product and simulation data of the product.
Preferably, according to the current operation data in the factory, the operation data are input into a corresponding digital twin model for optimizing the digital twin model;
the current operation data comprise current simulation data, current process flow data and current action data and motion data of the robot of the product.
Preferably, the specific method for constructing the digital twin model of the robot comprises the following steps,
s1, constructing a functional module of a robot, wherein the functional module comprises an action module and a motion module;
s2, an input interface and an output interface of the functional module are established;
s3, constructing and applying an ROS system, and utilizing at least one mechanism in Topic, service, action in the ROS system to interact among the functional modules;
s4, generating a digital twin model of the robot by using simulation software according to the functional module, the input interface and the output interface.
By adopting the technical scheme, compared with the prior art, the invention has the following beneficial effects.
According to the digital virtual factory building system, the industrial big data platform is deployed to store the sensing data, and the industrial big data platform is deployed based on the HDFS system, so that the sensing data can be compressed, the storage cost is reduced, the data receiving and transmitting speed can be improved, and the efficiency of building the digital twin model is improved.
The invention adopts the Process Mining algorithm to construct the Process flow digital twin model of the entity equipment, reduces the workload of manual modeling, and avoids the problems of low modeling efficiency and high cost of manual modeling and inaccurate modeling of the internal environment of the factory.
According to the invention, the digital twin model of the robot is constructed, and the ROS system is constructed and applied to realize interaction among all functional modules of the robot, so that the digital twin model of the robot is utilized to remotely monitor entity equipment.
The following describes the embodiments of the present invention in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. It is evident that the drawings in the following description are only examples, from which other drawings can be obtained by a person skilled in the art without the inventive effort. In the drawings:
FIG. 1 is a block diagram of a digitized virtual factory building system of the present invention.
It should be noted that these drawings and the written description are not intended to limit the scope of the inventive concept in any way, but to illustrate the inventive concept to those skilled in the art by referring to the specific embodiments.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions in the embodiments will be clearly and completely described with reference to the accompanying drawings in the embodiments of the present invention, and the following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention.
In the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the description of the present invention, it should be noted that, unless explicitly specified and limited otherwise, the terms "mounted," "connected," and "connected" are to be construed broadly, and may be either fixedly connected, detachably connected, or integrally connected, for example; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
As shown in FIG. 1, the invention provides a digital virtual factory building system, which comprises an acquisition module, a building module, a digital twin building module and a visual presentation module;
the acquisition module is used for acquiring sensing data of the factory, wherein the sensing data at least comprises one or more of simulation data of products in the factory, process flow data of equipment in the factory, and motion data of a robot in the factory;
the construction module is used for deploying an industrial big data platform, storing the sensing data in the factory on the industrial big data platform and sending the sensing data to the digital twin construction module;
the digital twin construction module is used for constructing a digital twin model according to the sensing data;
the visual presentation module is used for realizing the visualization of the digital twin model.
By deploying the industrial big data platform, the data of the entity equipment can be stored, the speed of receiving and transmitting the data can be improved, and the efficiency of constructing the digital twin model is improved.
The visual presentation module is realized based on the Unity technology, can divide the environment in the factory and present the environment in the factory in each area, so that immersive virtual reality experience is realized, in addition, the Unity technology supports a visual interface accessed by the mobile terminal, and the use experience sense of a user is improved.
Preferably, an industrial big data platform is deployed based on the HDFS, and an edge computing gateway is arranged for preprocessing various sensing data of the collected factory, and the preprocessed data is stored in the industrial big data platform based on the HDFS.
The HDFS system can compress the data of the entity equipment, and the storage cost is reduced. HDFS is a highly fault tolerant system suitable for deployment on inexpensive machines. HDFS can provide high throughput data access, and is well suited for applications on large data sets. HDFS relaxes a portion of the POSIX constraints to achieve the purpose of streaming file system data. And it provides high throughput access to data of applications suitable for those with very large data sets.
Preferably, the edge computing gateway has strong edge computing capability, and an industrial intelligent gateway of an embedded network operating system which supports technologies such as remote custom configuration, remote deployment, gateway state monitoring and the like, can realize real-time response of data, analysis and judgment of a data model and remote maintenance and downloading of equipment.
Preprocessing various types of sensor data of the collected factory comprises data cleaning, denoising and formatting.
Preferably, the industrial big data platform deployment based on the HDFS specifically comprises the steps of constructing an HDFS cluster, setting the number of servers of the HDFS cluster according to the data after preprocessing, storing the data after preprocessing in the servers of the HDFS cluster, and realizing interaction with an HDFS system by using an interface of an HDFS API.
According to the data after preprocessing, the server number of the HDFS cluster is set, a Standby architecture is adopted to configure NameNode (manager) and DataNode (worker), one HDFS cluster comprises one NameNode (manager) and a plurality of DataNode (worker), and the NameNode (manager) is mainly responsible for managing the HDFS file system, and specifically comprises directory structure management and block management. NameNode (manager) provides a server which always receives services passively, and four types of protocol interfaces are mainly provided. The DataNode (worker) is mainly used for storing data files, and the HDFS divides a file into blocks, and the blocks may be stored on one DataNode (worker) or multiple datanodes (workers).
The DataNode (worker) is responsible for the actual reading and writing of the underlying files, if the client program initiates a command to read files on HDFS, these files are first split into blocks, then the NameNode (manager) will tell the client which DataNode (worker) the block data is stored on, after which the client will interact directly with the DataNode (worker).
The industrial big data platform is deployed based on the HDFS, and the industrial big data platform is integrated with a Spark component for analyzing and calculating the stored data, and then storing the analyzed and calculated data again.
The Spark component comprises Spark SQL, spark Streaming and MLlib components, and interactive data analysis query is constructed by using the Spark SQL; constructing a real-time data processing pipeline by using Spark Streaming; and (5) constructing a machine learning model by using MLlib, and performing predictive analysis.
Preferably, the construction of the corresponding digital twin model according to the sensing data comprises the construction of the digital twin model of the process flow according to the process flow data of the equipment in the factory, wherein the process flow data of the equipment in the factory comprises the data of the charging time; constructing a product digital twin model according to simulation data of products in a factory; and constructing a digital twin model of the robot according to the motion data and the action data of the robot in the factory.
And cleaning the collected process flow data of the equipment in the factory, and filtering useless or repeated data.
Preferably, a Process Mining algorithm is used to construct a Process flow digital twin model of the physical device. The Process Mining algorithm specifically comprises the steps of importing the preprocessed Process flow data into the Process Mining algorithm, obtaining a Process flow digital twin model through a Process Discovery algorithm, and evaluating the Process flow digital twin model through a Conformance Checking algorithm. And continuously optimizing the flow model by using an Enhancement algorithm. The Process Mining algorithm is adopted to automatically construct a Process flow digital twin model of the entity equipment, so that the problems of low modeling efficiency of manual modeling and inaccurate modeling of the internal environment of a factory are avoided.
Preferably, the design diagram of the product is collected, and a digital twin model of the product is generated by adopting a GANs implementation mode according to the design diagram of the product and simulation data of the product.
Collecting design drawings of products in a factory, wherein the design drawings comprise CAD files, and still cleaning simulation data of the products in the factory to extract effective data; the method comprises the steps of designing a generator and a discriminator by using the implementation mode of GANs, wherein the generator is used for inputting design data of a product and outputting virtual design data of the product, and the discriminator is used for judging authenticity of the input design data of the product and the output virtual design data. In addition, the Wasserstein GAN can improve a product digital twin model and increase stability.
Preferably, according to the current operation data in the factory, the operation data are input into a corresponding digital twin model for optimizing the digital twin model;
the current operation data comprise current simulation data, current process flow data and current action data and motion data of the robot of the product.
Preferably, the specific method for constructing the digital twin model of the robot comprises the following steps,
s1, constructing a functional module of a robot, wherein the functional module comprises an action module and a motion module;
s2, an input interface and an output interface of the functional module are established;
s3, constructing and applying an ROS system, and utilizing at least one mechanism in Topic, service, action in the ROS system to interact among the functional modules;
s4, generating a digital twin model of the robot by using simulation software according to the functional module, the input interface and the output interface.
Preferably, gazebo simulation software may be employed.
Preferably, the digital twin model of the robot may also be optimized based on the data of the current functional module of the robot.
The foregoing description is only a preferred embodiment of the present invention, and the present invention is not limited to the above-mentioned embodiment, but is not limited to the above-mentioned embodiment, and any simple modification, equivalent change and modification made by the technical matter of the present invention can be further combined or replaced by the equivalent embodiment without departing from the scope of the technical solution of the present invention.
Claims (10)
1. The digital virtual factory building system is characterized by comprising an acquisition module, a building module, a digital twin building module and a visual presentation module;
the acquisition module is used for acquiring sensing data of the factory, wherein the sensing data at least comprises one or more of simulation data of products in the factory, process flow data of equipment in the factory, and motion data of a robot in the factory;
the construction module is used for deploying an industrial big data platform, storing the sensing data in the factory on the industrial big data platform and sending the sensing data to the digital twin construction module;
the digital twin construction module is used for constructing a digital twin model according to the sensing data;
the visual presentation module is used for realizing the visualization of the digital twin model.
2. The digitized virtual factory building system of claim 1 wherein an industrial big data platform is deployed based on an HDFS system, and an edge computing gateway is provided to preprocess the collected sensor data, and the preprocessed data is stored on the industrial big data platform.
3. The system for constructing a digital virtual factory according to claim 2, wherein the construction of the industrial big data platform based on the HDFS system specifically comprises constructing an HDFS cluster, setting the number of servers of the HDFS cluster according to the data after preprocessing, storing the data after preprocessing in the servers of the HDFS cluster, and using an interface to interact with the HDFS system.
4. The digitized virtual factory building system of claim 3 wherein building an industrial large data platform based on the HDFS system further comprises integrating a Spark component on the industrial large data platform for parallel analysis of stored data.
5. The digitized virtual plant construction system of claim 2 wherein constructing a digital twin model from the sensed data comprises constructing a process flow digital twin model from process flow data of equipment within the plant; constructing a product digital twin model according to simulation data of products in a factory; and constructing a digital twin model of the robot according to the motion data and the motion data of the robot in the factory.
6. The digitized virtual factory building system of claim 5 wherein a Process flow digital twin model of the physical device is built using a Process Mining algorithm.
7. The digitized virtual plant construction system of claim 6 wherein the Process flow data is imported in a Process Mining algorithm, a Process flow digital twin model is obtained by a Process Discovery algorithm, and the Process flow digital twin model is evaluated by a Conformance Checking algorithm.
8. The system of claim 5, wherein the digital twinning model is generated by using a gain implementation based on the design drawing of the product and the simulation data of the product.
9. The digitized virtual plant construction system of claim 8 wherein the input to the corresponding digital twin model is based on current operational data within the plant for optimization of the digital twin model;
the current operation data comprise current simulation data, current process flow data and current action data and motion data of the robot of the product.
10. The digitized virtual plant construction system of claim 5 wherein the specific method of constructing a digital twin model of a robot comprises,
s1, constructing a functional module of a robot, wherein the functional module comprises an action module and a motion module;
s2, an input interface and an output interface of the functional module are established;
s3, constructing and applying an ROS system, and utilizing at least one mechanism in Topic, service, action in the ROS system to interact among the functional modules;
s4, generating a digital twin model of the robot by using simulation software according to the functional module, the input interface and the output interface.
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