CN117311982A

CN117311982A - Digital twin body display method, system, device and computer equipment

Info

Publication number: CN117311982A
Application number: CN202311409916.8A
Authority: CN
Inventors: 姚维兵; 康冬华; 吴车; 贺毅; 左志军; 丘邦超; 马鸣韬; 邹永标
Original assignee: Guangzhou Mino Equipment Co Ltd
Current assignee: Guangzhou Mino Equipment Co Ltd
Priority date: 2023-10-27
Filing date: 2023-10-27
Publication date: 2023-12-29

Abstract

The present application relates to a digital twin exhibiting method, system, apparatus, computer device, storage medium and computer program product. The method comprises the following steps: according to a digital twin body display request sent by a terminal, acquiring a target digital twin body to be displayed from a stored digital twin body, and determining the behavior type of entity equipment corresponding to the target digital twin body; the stored digital twin is constructed by a cloud server and issued to an edge server; determining a behavior model corresponding to the target digital twin body according to the behavior type; determining a target motion model corresponding to the behavior model, and acquiring motion parameters required by the target motion model; and returning the motion parameters, the target motion model and the target digital twin body to the terminal, so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model. By adopting the method, efficient digital twin body display can be realized.

Description

Digital twin body display method, system, device and computer equipment

Technical Field

The present application relates to the field of digital twinning technology, and in particular, to a digital twinning method, a system, an apparatus, a computer device, a storage medium, and a computer program product.

Background

With the development of the intelligent manufacturing field, digital twin technology will be applied to the industrial manufacturing field more and more frequently.

However, in the industrial manufacturing field, the number of entity devices required to construct the digital twin is large, the types are rich, and the corresponding algorithm models are also large, so that more storage and operation performances are required to construct the corresponding digital twin and training algorithm models. In order to achieve better storage and operation performance, a cloud server is generally adopted to complete corresponding work, however, the mode cannot meet the requirement of digital twinning on instantaneity, and the problem of low display efficiency of the digital twinning is caused.

Disclosure of Invention

Based on this, it is necessary to provide a digital twin exhibiting method, system, apparatus, computer device, computer readable storage medium and computer program product for the technical problem of poor real-time performance of digital twin.

In a first aspect, the present application provides a digital twin exhibition method applied to an edge server. The method comprises the following steps:

according to a digital twin body display request sent by a terminal, acquiring a target digital twin body to be displayed from a stored digital twin body, and determining the behavior type of entity equipment corresponding to the target digital twin body; the stored digital twin is constructed by a cloud server and issued to the edge server;

Determining a behavior model corresponding to the target digital twin according to the behavior type;

determining a target motion model corresponding to the behavior model, and acquiring motion parameters required by the target motion model;

and returning the motion parameters, the target motion model and the target digital twin body to the terminal, so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model.

In one embodiment, the determining the behavior type of the entity device corresponding to the target digital twin includes:

acquiring real-time monitoring information of the entity equipment;

determining real-time state change information of the entity equipment according to the real-time monitoring information;

and determining the behavior type of the entity equipment corresponding to the real-time state change according to the real-time state change information.

In one embodiment, after acquiring the real-time monitoring information of the entity device, the method further includes:

processing the real-time monitoring information by adopting a state monitoring model deployed at the edge server to obtain a real-time state result of the entity equipment; the state monitoring model is trained by a cloud server and issued to the edge server;

And sending the real-time state result to the terminal, so that the terminal displays the real-time state result.

according to a state prediction request sent by a terminal, predicting the real-time monitoring information by adopting a state prediction model deployed at the edge server to obtain a predicted state result of the entity equipment; the state prediction model is trained by a cloud server and issued to the edge server;

and returning the predicted state result to the terminal, so that the terminal displays the predicted state result.

In one embodiment, after obtaining the predicted state result of the entity device, the method further includes:

and sending the real-time monitoring information and the predicted state result to the cloud server, so that the cloud server updates the state prediction model based on the real-time monitoring information and the predicted state result.

In a second aspect, the present application further provides a digital twin body display method, which is applied to a cloud server. The method comprises the following steps:

constructing a digital twin;

Issuing the digital twin body to an edge server; the edge server is used for acquiring a target digital twin body to be displayed from a received digital twin body according to a digital twin body display request sent by a terminal, determining a behavior type of entity equipment corresponding to the target digital twin body, determining a behavior model corresponding to the target digital twin body according to the behavior type, determining a target motion model corresponding to the behavior model, acquiring motion parameters required by the target motion model, and returning the motion parameters, the target motion model and the target digital twin body to the terminal to enable the terminal to display the analog motion of the target digital twin body based on the motion parameters and the target motion model.

In one embodiment, the digital twin comprises a digital twin of a plurality of physical devices;

the construction of a digital twin comprising:

acquiring equipment information of each entity equipment;

determining a first association relation between each entity device and the upper and lower level components and a second association relation between each level of components and the same level of components in each entity device according to the device information;

Constructing a first association relation model of the first association relation by adopting an automatic mark language, and constructing a second association relation model of the second association relation by adopting a map database;

and constructing a digital twin body of each entity device according to the first association relation model and the second association relation model.

In a third aspect, the present application also provides a digital twin display system. The system comprises: the edge server and the cloud server;

the cloud server is used for constructing a digital twin body and transmitting the digital twin body to the edge server;

the edge server is used for acquiring a target digital twin body to be displayed from a received digital twin body according to a digital twin body display request sent by a terminal, determining a behavior type of entity equipment corresponding to the target digital twin body, determining a behavior model corresponding to the target digital twin body according to the behavior type, determining a target motion model corresponding to the behavior model, acquiring motion parameters required by the target motion model, and returning the motion parameters, the target motion model and the target digital twin body to the terminal to enable the terminal to display the analog motion of the target digital twin body based on the motion parameters and the target motion model.

In a fourth aspect, the present application further provides a digital twin body display device, which is applied to an edge server. The device comprises:

the information acquisition module is used for acquiring a target digital twin body to be displayed from the stored digital twin body according to a digital twin body display request sent by the terminal, and determining the behavior type of entity equipment corresponding to the target digital twin body; the stored digital twin is constructed by a cloud server and issued to the edge server;

the model determining module is used for determining a behavior model corresponding to the target digital twin body according to the behavior type;

the parameter determining module is used for determining a target motion model corresponding to the behavior model and acquiring motion parameters required by the target motion model;

and the data sending module is used for returning the motion parameters, the target motion model and the target digital twin body to the terminal, so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model.

In a fifth aspect, the present application further provides a digital twin body display device, which is applied to a cloud server. The device comprises:

A construction module for constructing a digital twin;

the sending module is used for sending the digital twin body to an edge server; the edge server is used for acquiring a target digital twin body to be displayed from a received digital twin body according to a digital twin body display request sent by a terminal, determining a behavior type of entity equipment corresponding to the target digital twin body, determining a behavior model corresponding to the target digital twin body according to the behavior type, determining a target motion model corresponding to the behavior model, acquiring motion parameters required by the target motion model, and returning the motion parameters, the target motion model and the target digital twin body to the terminal to enable the terminal to display the analog motion of the target digital twin body based on the motion parameters and the target motion model.

In a sixth aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

In a seventh aspect, the present application also provides a computer device. The computer device comprises a memory storing a computer program and a processor which when executing the computer program performs the steps of:

constructing a digital twin;

In an eighth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

In a ninth aspect, the present application also provides a computer-readable storage medium. The computer readable storage medium having stored thereon a computer program which when executed by a processor performs the steps of:

Constructing a digital twin;

In a tenth aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

In an eleventh aspect, the present application also provides a computer program product. The computer program product comprises a computer program which, when executed by a processor, implements the steps of:

constructing a digital twin;

According to the digital twin body display method, the system, the device, the computer equipment, the storage medium and the computer program product, firstly, according to a digital twin body display request sent by a terminal, a target digital twin body to be displayed is obtained from a stored digital twin body, the behavior type of entity equipment corresponding to the target digital twin body is determined, the stored digital twin body is constructed by a cloud server and is issued to an edge server, and work with high storage and performance requirements is completed by the cloud server, so that the construction accuracy and high efficiency of the digital twin body are ensured; then, determining a behavior model corresponding to the target digital twin body according to the behavior type; then, determining a target motion model corresponding to the behavior model, acquiring motion parameters required by the target motion model, obtaining the motion model based on the behavior model correspondence, and displaying the simulated motion of the digital twin body by using the basic motion model, so that the simulated motion of the digital twin body can be displayed more accurately; and finally, returning the motion parameters, the target motion model and the target digital twin body to the terminal, so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model, and the edge server finishes the real-time processing of the analog motion data and then sends the processed analog motion data to the terminal for display, thereby effectively reducing the time delay of data transmission and more efficiently completing the analog motion display of the digital twin body under the condition of ensuring the accuracy. In the method, the edge server builds and issues the digital twin in advance through the cloud server and combines the behavior types of the entity equipment, so that real-time and low-delay digital twin display is realized.

Drawings

FIG. 1 is an application environment diagram of a digital twin exhibition method in one embodiment;

FIG. 2 is a flow diagram of a digital twin display method in one embodiment;

FIG. 3 is an exemplary diagram of a finite state machine of a behavioral model in one embodiment;

FIG. 4 is a flow diagram of steps for determining a behavior type in one embodiment;

FIG. 5 is a flow chart of a digital twins presentation method in another embodiment;

FIG. 6 is a diagram of an example document construction of part AML and device AML in one embodiment;

FIG. 7 is an exemplary diagram of the final file composition of a plant AML in one embodiment;

FIG. 8 is an exemplary diagram of a second association in one embodiment;

FIG. 9 is a digital twinning application floor schematic of one physical device in one embodiment;

FIG. 10 is a block diagram of the architecture of a digital twinning system in one embodiment;

FIG. 11 is a schematic diagram of a Bian Yun collaborative architecture of a digital twinning system in one embodiment;

FIG. 12 is a schematic architecture diagram of an edge server deployment package of the digital twinning system in one embodiment;

FIG. 13 is a schematic diagram of cloud-edge interactions of a digital twinning system involving algorithmic model data, in one embodiment;

FIG. 14 is a digital twin body presentation flow diagram of a digital twin system in one embodiment;

FIG. 15 is a data flow diagram in a digital twin body presentation of a digital twin system in one embodiment;

FIG. 16 is a block diagram of a digital twin display device in one embodiment;

FIG. 17 is a block diagram of a digital twin display device in another embodiment;

fig. 18 is an internal structural view of a computer device in one embodiment.

Detailed Description

In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.

The digital twin body display method provided by the embodiment of the application can be applied to an application environment shown in fig. 1. The edge server 102 communicates with the cloud server 104, the terminal 106, and the entity device 108 through a network. The data storage system may store data to be processed by the edge server 102 and the cloud server 104, respectively. The edge server 102 obtains a target digital twin to be displayed from a stored digital twin according to a digital twin display request sent by the terminal 106, and determines a behavior type of entity equipment 108 corresponding to the target digital twin, wherein the stored digital twin is constructed by the cloud server 104 and issued to the edge server 102; the edge server 102 determines a behavior model corresponding to the target digital twin according to the behavior type; the edge server 102 determines a target motion model corresponding to the behavior model and acquires motion parameters required by the target motion model; the edge server 102 returns the motion parameters, the target motion model, and the target digital twin to the terminal 106, causing the terminal 106 to exhibit simulated motion of the target digital twin based on the motion parameters and the target motion model. The terminal 102 may be, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, and the like. The edge server 102 and the cloud server 104 may be implemented as separate servers or as a server cluster composed of a plurality of servers.

In one embodiment, as shown in fig. 2, a digital twin body exhibition method is provided, and the method is applied to the edge server 102 in fig. 1 for illustration, and includes the following steps:

step S201, according to a digital twin body display request sent by a terminal, acquiring a target digital twin body to be displayed from a stored digital twin body, and determining the behavior type of entity equipment corresponding to the target digital twin body.

The stored digital twin is constructed by a cloud server and issued to an edge server.

The digital twin is to fully utilize data such as a physical model, sensor update, operation history and the like, integrate simulation processes of multiple disciplines, multiple physical quantities, multiple scales and multiple probabilities, and complete mapping in a virtual space so as to reflect the full life cycle process of corresponding entity equipment. The mapping object of the entity device in the virtual space is the digital twin.

Illustratively, the user enters a digital twin exhibition request at the terminal 106 for the target entity device. After receiving the digital twin body display request, the edge server 102 queries and acquires a corresponding target digital twin body from a database according to the entity equipment identifier in the digital twin body display request; at the same time, the edge server 102 collects data of the entity device 108 in real time, thereby determining the current behavior type of the entity device. The collected data of the entity device 108 may contain user operational instructions for the entity device 108.

Step S202, determining a behavior model corresponding to the target digital twin according to the behavior type.

Illustratively, the edge server 102 determines a behavior model of the target digital twin from a database based on the association according to the current behavior type of the entity device 108. Each entity device is provided with a preset behavior type, such as grabbing behaviors of a mechanical arm; the digital twin corresponding to the entity equipment has a behavior model corresponding to the behavior type, and the behavior model is prestored in a database corresponding to the edge server. As shown in fig. 3, a finite state machine may be used for modeling to obtain a behavior model, where a behavior state group and an instruction group are defined in the behavior model. Finite state automata (FSM finite state machine or FSA finite state automaton) is a computational model abstracted for studying the computational process of finite memory and certain language classes, the finite state automata has a finite number of states, each state can be migrated to zero or more states, an input string decides which state to perform the migration, the finite state automata can be represented as a directed graph, and the finite state automata is a study object of automata theory.

Referring to the example of FIG. 3, a schematic diagram of the behavior state of an entity device in one example based on instruction transitions is shown.

Each behavior model is formed by combining a plurality of motion models, and the motion models can be divided into a linear motion model, a curve motion model, an axis motion model, a cylinder motion model and other motion models. Wherein, the linear motion is the forward and backward motion of the straight line, and can be non-uniform motion; a curvilinear motion is a motion that follows a curvilinear trajectory; the shaft type motion is motion based on a fixed rotation shaft; cylinder movement is movement based on a cylinder, such as piston movement; other movements are other minor movements than the above-described movements.

Step S203, determining a target motion model corresponding to the behavior model, and acquiring motion parameters required by the target motion model.

Illustratively, the server 102 determines a target motion model contained in the current behavior model of the digital twin and determines motion parameters required for the target motion model from data collected from the physical device in real time. The motion parameters comprise a track, a speed, an acceleration, an initial position and a motion formula. The speed and the acceleration in the linear motion model are respectively linear speed and linear acceleration; the speed and the acceleration in the curve motion model are respectively curve speed and curve acceleration; the speed and acceleration in the shaft motion model are angular speed and angular acceleration, respectively. The cylinder is mainly used as a driving source to drive the driven object to move, so that the cylinder can realize linear movement and curved movement, the driven object also correspondingly performs linear movement and curved movement, and the movement parameters of the cylinder and the driven object are also the movement parameters of a linear movement model or a curved movement model.

And step S204, returning the motion parameters, the target motion model and the target digital twin body to the terminal, so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model.

Illustratively, the edge server 102 returns the motion parameters, the target motion model, and the target digital twins to the terminal 106, and the terminal 106 exposes the target digital twins corresponding to the motion of the physical device to simulate the motion based on the motion parameters and the target motion model.

According to the digital twin body display method, firstly, according to a digital twin body display request sent by a terminal, a target digital twin body to be displayed is obtained from a stored digital twin body, the behavior type of entity equipment corresponding to the target digital twin body is determined, the stored digital twin body is constructed by a cloud server and is issued to an edge server, and work with high storage and performance requirements is completed by the cloud server, so that the construction accuracy and high efficiency of the digital twin body are ensured; then, determining a behavior model corresponding to the target digital twin body according to the behavior type; then, determining a target motion model corresponding to the behavior model, acquiring motion parameters required by the target motion model, obtaining the motion model based on the behavior model correspondence, and displaying the simulated motion of the digital twin body by using the basic motion model, so that the simulated motion of the digital twin body can be displayed more accurately; and finally, returning the motion parameters, the target motion model and the target digital twin body to the terminal, so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model, and the edge server finishes the real-time processing of the analog motion data and then sends the processed analog motion data to the terminal for display, thereby effectively reducing the time delay of data transmission and more efficiently completing the analog motion display of the digital twin body under the condition of ensuring the accuracy. In the method, the edge server builds and issues the digital twin in advance through the cloud server and combines the behavior types of the entity equipment, so that real-time and low-delay digital twin display is realized.

In one embodiment, as shown in fig. 4, the determining the behavior type of the entity device corresponding to the target digital twin in the step S201 may further be implemented by the following steps:

step S401, acquiring real-time monitoring information of entity equipment;

step S402, determining real-time state change information of entity equipment according to real-time monitoring information;

step S403, determining the behavior type of the entity equipment corresponding to the real-time state change according to the real-time state change information.

The edge server obtains real-time monitoring information of the entity device in real time through devices such as a sensor and a monitoring element, and after obtaining the real-time monitoring information, the edge server can analyze and forward data by adopting a stream processing engine eKupper. The eKupper is a lightweight data analysis and stream processing engine of the Internet of things and can run on edge equipment with limited resources. The main goal of the eKupper is to provide a streaming media software framework at the edge, and the rule engine of the eKupper allows users to provide SQL-based or graphics-based (similar to Node-RED) rules to create edge analysis applications within a few minutes. And the edge server determines real-time state change information of the entity equipment, namely the current start-stop state, operation stage and other information of the entity equipment according to the real-time monitoring information. Then, the edge server queries a pre-stored mapping relation based on the implementation state change information, and can determine the behavior type of the entity equipment currently operated. In addition, the real-time monitoring information at least comprises running instruction information of the user on the entity equipment, and the edge server can directly determine the current running behavior type of the entity equipment based on the running instruction information and other real-time monitoring information.

In this embodiment, by acquiring real-time monitoring information of the entity device, actual data about the current state, performance and operation condition of the device can be obtained, so as to determine the behavior type of the entity device, and provide data support for implementing real-time mapping between the entity device and the digital twin body.

In one embodiment, after the acquiring the real-time monitoring information of the entity device in step S401, the method further includes: processing the real-time monitoring information by adopting a state monitoring model deployed on an edge server to obtain a real-time state result of the entity equipment; the state monitoring model is trained by the cloud server and issued to the edge server; and sending the real-time state result to the terminal, so that the terminal displays the real-time state result.

The method includes that after the edge server obtains the real-time monitoring information of the entity device, the real-time monitoring information can be input into a state monitoring model which is pre-deployed in the edge server in response to a state display request of the entity device, and output of a real-time state result of the entity device is obtained. The real-time status results may include anomaly detection results, performance metrics, etc. of the entity device. And then the edge server can send the real-time state result, the digital twin body, the motion model and the motion parameters to the terminal together, so that the terminal can display the real-time state result of the entity equipment at the same time. In addition, the state monitoring model is obtained by constructing and training a cloud server, and after training is completed, the state monitoring model is sent to an edge server.

In the embodiment, training of the state monitoring model is completed through the cloud server and is issued to the edge server, so that more complex model training is performed on the cloud server with better performance, and a more accurate model is obtained; the state monitoring model is deployed on the edge server, so that communication delay between the user side (namely the terminal) and the cloud server can be reduced, and real-time monitoring and timely analysis of the entity equipment are facilitated. The model training and the application are distributed on the cloud server and the edge server, so that the advantages of cloud-edge coordination are fully exerted, the quality of the model is guaranteed, the delay is reduced, and a more comprehensive digital twin solution is provided.

In one embodiment, after the acquiring the real-time monitoring information of the entity device in step S401, the method further includes: according to a state prediction request sent by a terminal, predicting real-time monitoring information by adopting a state prediction model deployed at an edge server to obtain a predicted state result of entity equipment; the state prediction model is trained by a cloud server and issued to an edge server; and returning the predicted state result to the terminal, so that the terminal displays the predicted state result.

The edge server may also respond to a state prediction request of the user for the entity device, and after acquiring the real-time monitoring information of the entity device, input the real-time monitoring information into a state prediction model pre-deployed in the edge server to obtain an output of a predicted state result of the entity device. The predicted status results may include risk of failure, performance degradation, predicted lifetime of the physical device, etc. And then the edge server sends the predicted state result to the terminal, so that the terminal can also display the predicted state result of the entity equipment. In addition, the state prediction model is obtained by constructing and training a cloud server, and after training is completed, the state prediction model is sent to an edge server.

In the embodiment, training of the state prediction model is completed through the cloud server and is issued to the edge server, so that more complex model training is performed on the cloud server with better performance, and a more accurate model is obtained; the state prediction model is deployed on the edge server, so that communication delay between a user side (namely a terminal) and the cloud server can be reduced, more timely prediction analysis is conveniently carried out on the entity equipment, and equipment faults and performance degradation are effectively avoided. The model training and the application are distributed on the cloud server and the edge server, so that the advantages of cloud-edge coordination are fully exerted, the quality of the model is guaranteed, the delay is reduced, and a more comprehensive digital twin solution is provided.

In one embodiment, after obtaining the predicted status result of the entity device, the method further includes: and sending the real-time monitoring information and the predicted state result to the cloud server, so that the cloud server updates the state prediction model based on the real-time monitoring information and the predicted state result.

The edge server sends the real-time monitoring information and the predicted state result to the cloud server after the predicted state result of the entity device is obtained by applying the state prediction model, so that the cloud server takes the received real-time monitoring information and the received predicted state result as a new model training sample, trains and updates the state prediction model, and obtains a more accurate and comprehensive state prediction model. Similarly, after the real-time state results of the edge server and other entity devices, if the real-time state results are different from the actual state results input by the user, the real-time monitoring data and the actual state results can be sent to the cloud server, so that the cloud server takes the received real-time monitoring data and the actual state results as new model training samples to train and update the state monitoring model.

In this embodiment, the real-time monitoring information and the prediction state result are sent to the cloud server, so that the cloud server can iterate and optimize the state prediction model continuously, adapt to the actual situation of continuous change, and ensure the adaptability and practicality of the model.

In one embodiment, as shown in fig. 5, a digital twin body exhibition method is provided, and the method is applied to the cloud server 104 in fig. 1 for illustration, and includes the following steps:

in step S501, a digital twin is constructed.

Step S502, the digital twin body is issued to an edge server; the edge server is used for acquiring a target digital twin body to be displayed from the received digital twin body according to a digital twin body display request sent by the terminal, determining the behavior type of entity equipment corresponding to the target digital twin body, determining a behavior model corresponding to the target digital twin body according to the behavior type, determining a target motion model corresponding to the behavior model, acquiring motion parameters required by the target motion model, and returning the motion parameters, the target motion model and the target digital twin body to the terminal so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model.

In one embodiment, a digital twin comprising a plurality of physical devices therein; the step S501 constructs a digital twin body, and further includes: acquiring equipment information of each entity equipment; according to the equipment information, determining a first association relation between each entity equipment and the upper and lower level components and a second association relation between each level of components and the same level of components in each entity equipment; constructing a first association relation model of a first association relation by adopting an automatic mark language, and constructing a second association relation model of a second association relation by adopting a map database; and constructing a digital twin body of each entity device according to the first association relation model and the second association relation model.

Among them, the automated markup language (Automation Markup Language, AML) is a data file for storing and exchanging plant engineering information, which is an XML-based data exchange format for plant engineering data. AML is intended to support data exchange between heterogeneous engineering devices, with the aim of exchanging data interconnections in different fields, such as mechanical engineering, electrical engineering, machining engineering, process control engineering, HMI, PLC programming, robot programming, etc. It can be applied to all industrial fields requiring data exchange, such as discrete industries or process industries.

Wherein the profile database may employ a Neo4j database that stores structured data on a network rather than in a table. The network (called graph from the mathematical point of view) is a flexible data structure, and can apply a more agile and rapid development mode, which not only supports the data expression of the graph, but also supports the derivative function of the graph, such as searching by the node group according to the relevance.

It should be noted that, in the industrial field, a technical file describing a composition of an enterprise product, that is, a file describing a product structure in a data format, is a product structure data file that can be identified by a computer, and is also a dominant file of ERP (Enterprise Resource Planning ). BOM makes the system recognize the product structure and is also the tie for connecting and communicating various business of enterprises. The types of BOM in ERP systems mainly include 5 types: the system comprises a condensed BOM, a summarized BOM, a back check BOM, a cost BOM and a planning BOM.

Illustratively, each level of BOM in industrial production may be divided into plant-area-production line-equipment-parts, i.e., one plant is composed of multiple plants and one plant is composed of multiple areas … …

AML is adopted to describe the attribute and the capability of each level of BOM, namely, the first association relation between each entity device and the upper and lower level components. The composition of an AML file is shown in table 1.

TABLE 1 composition of an AML File

As shown in fig. 6, an exemplary diagram is formed for a document of component AML and device AML. The part is not detachable, so that an Internalelement module in the part AML comprises subelements; the intel element module in the device AML includes the component group of the device as a sub AML component of the module. Similarly, the file constitution of the production line AML, the area AML, the workshop AML and the factory AML can be obtained, and the description is omitted here.

The AML description of each level of BOM is defined according to the above, and then the AML description is carried out on each level of BOM by simplifying the description of AML language, so that the AML description is simplified into attributes and sub-element groups, and the whole factory (or other levels of BOM) is realized by single AML description. Thus, describing the final plant-level AML composition through AML files of each level of BOM is shown in fig. 7.

And describing the transverse relation of each entity device by adopting a map database of Neo4j, namely, the second association relation between each level of components and the same level of components in each entity device. As shown in fig. 8, a plurality of parts are arranged in one device, and the parts have assembly relations, such as part 1, part 2 and part 3; for devices, there is a cooperative relationship between devices 1, 2, 3.

Referring to fig. 9, the cloud server constructs a BOM relationship model of the entity device, that is, a first relationship model, by using AML, and describes a relationship model between sub-components of the entity device, that is, a second relationship model, by using Neo4 j. Based on the first association relation model and the second association relation model, a digital twin body corresponding to the entity equipment is constructed and obtained, and the digital twin body is sent to the terminal, so that the terminal realizes the application landing of the digital twin based on the digital twin body.

In this embodiment, a hierarchical relationship and an attribute relationship of the BOM are described based on AML language, and a peer relationship of the components described based on the spectrum database Neo4j may be represented, which may represent a combination relationship of the upper layer to the lower layer, or may represent an assembly relationship between the components. By the method, the association relation of the entity equipment is accurately described, and further, an accurate digital twin body can be established, so that the application of the digital twin body has better practicability.

In one embodiment, as shown in fig. 10, there is further provided a digital twin system to which the above digital twin body display method is applied, including: cloud server 1001 and edge server 1002;

the cloud server 1001 is configured to construct a digital twin, and send the digital twin to the edge server 1002;

the edge server 1002 is configured to obtain, according to a digital twin exhibition request sent by the terminal 1003, a target digital twin to be exhibited from the received digital twin, determine a behavior type of an entity device corresponding to the target digital twin, determine a behavior model corresponding to the target digital twin according to the behavior type, determine a target motion model corresponding to the behavior model, obtain motion parameters required by the target motion model, and return the motion parameters, the target motion model, and the target digital twin to the terminal 1003, so that the terminal 1003 exhibits an analog motion of the target digital twin based on the motion parameters and the target motion model.

According to the system, the edge cloud cooperation technical scheme is achieved, bian Yun cooperation means that cloud and edges are connected, cloud capacity is extended to edge nodes close to equipment, cloud and edge side data are linked, and remote control, data analysis, intelligent decision making and the like of the edge nodes are achieved. The method comprises a plurality of synergies such as resource synergies, application synergies, data synergies, intelligent synergies and the like. Bian Yun is cooperative in nature to require that the edge node can efficiently manage resources and, in conjunction with the cloud, receive or initiate resource scheduling for the central cloud node.

Illustratively, as shown in fig. 11, a schematic diagram of a Bian Yun collaborative architecture of the system in this embodiment includes a cloud module, an edge module, and a container module. The cloud module is used for constructing an algorithm library and a digital twin application group and carrying out packing and issuing work of an edge application package; the edge module is used for deploying edge application to support the construction of a digital twin system; the container module is used to provide support for deployment of the application through the containerization technique.

Among them, heterogeneous computing (Heterogeneous Computing) can have the advantages of high performance computing power, good scalability, and high computing resource utilization.

K8s (Kubernetes) is an open source container cluster management system from the Google cloud platform for automatically deploying, expanding and managing containerized applications. The system builds a dispatch service for a container based on Docker.

The Harbor is a mirror image warehouse and has the main functions of mirror image storage, mirror image management and mirror image distribution. Each warehouse may contain multiple images, distinguished by labels. Typically, when using the mirror image, three elements of the mirror image warehouse type, the warehouse application, and the specific application version are fully considered.

Dock is an open-source application container engine, which can package user applications and rely on packages to a portable container in a unified way, then issue the packages to any server (including popular Linux machines and windows machines) with dock engine, and also can implement virtualization. The containers are completely using a sandbox mechanism without any interface to each other (an app like an iPhone). Almost no performance overhead, can be easily run in machines and data centers. Most importantly, container technology is not dependent on any language, framework, system.

The edge server can adopt edge Foundation technology as basic support, and edge Foundation is an open-source neutral edge computing micro-service framework irrelevant to hardware and an operating system of a Linux foundation, and is used for unifying an ecosystem of an industrial Internet of things edge computing solution. The architecture diagram of the edge server deployment package established in this embodiment is shown in fig. 12. The system foundation server group is used for supporting and constructing a foundation module; the deployment configuration group is used for carrying out configuration work of deployment parameters; the basic configuration set is used for running installation packages and script supports of the dockers; the application image group is used to provide all application component images for edge deployment.

The cloud server adopts a stability technology, an expansibility technology, a component library technology and other technologies to complete technical support. The stability technology is used for carrying out cluster management and monitoring on K8s to ensure the stability of a single application; the expansibility technology is used for dynamically expanding K8s support application; the component library technology is used for providing construction support for the component library by the Harbor; other techniques are used for technical support such as management and monitoring of containers.

A cloud-edge interaction schematic diagram of the system related to algorithm model data in the embodiment is shown in FIG. 13.

Fig. 14 is a schematic flow chart of a digital twin body exhibition in the present embodiment, wherein the related components include a streaming engine component (ekuier), a motion model component, and a behavior model component. The stream processing engine component is used for preprocessing data and realizing functions of data forwarding, secondary forwarding and the like; the motion model component is used for displaying motion mechanism data of the component through a motion model so as to support the display of a front-end page and support upper-layer applications based on the motion model, for example: interference detection; the behavior model component is used for exhibiting behavior mechanism data of the component through the behavior model to support upper-layer applications based on the behavior model, for example: virtual debugging based on a process step. Wherein the data flow is shown with reference to fig. 15. The original data is forwarded through the rubbitmq, the streaming processing of the data is carried out on the ekuiper level, the data forwarding is carried out on the behavior model again, and the digital twin body construction and display of the data are finally realized through the motion model. RabbitMQ is open source message broker software (also known as message oriented middleware) implementing Advanced Message Queuing Protocol (AMQP), rabbitMQ servers are written in Erlang's language, while clusters and failover are built on an open telecommunications platform framework, all major programming languages have a client library that communicates with the broker interface. AMQP (Advanced Message Queuing Protocol) is an application layer standard advanced message queue protocol for providing unified message service, is an open standard of the application layer protocol, is designed for message-oriented middleware, can transmit messages between a client and the message middleware based on the protocol, is not limited by different products of the client/the middleware, different development languages and other conditions, and is realized by RabbitMQ and the like in Erlang.

In this embodiment, the predictive functionality is provided after the AI algorithm model is deployed on the edge server. According to the model, AI of a plurality of side components can be achieved, and the function of cloud training and side prediction is achieved. Because the cloud has more resources, the cloud can store data, the edge performs the function of loading the data model, the edge uses fewer resources through simple loading prediction, the edge collects and uploads the data, the cloud is subjected to deep support of data retraining, and the condition of model perfection is provided. After cloud edge cooperation is completed, a self-perfecting function is provided, and the accuracy of an AI prediction algorithm model can be dynamically improved.

It should be understood that, although the steps in the flowcharts related to the embodiments described above are sequentially shown as indicated by arrows, these steps are not necessarily sequentially performed in the order indicated by the arrows. The steps are not strictly limited to the order of execution unless explicitly recited herein, and the steps may be executed in other orders. Moreover, at least some of the steps in the flowcharts described in the above embodiments may include a plurality of steps or a plurality of stages, which are not necessarily performed at the same time, but may be performed at different times, and the order of the steps or stages is not necessarily performed sequentially, but may be performed alternately or alternately with at least some of the other steps or stages.

Based on the same inventive concept, the embodiments of the present application also provide two digital twin body exhibition apparatuses for implementing the above-mentioned digital twin body exhibition method. The implementation of the solution provided by the device is similar to that described in the above method, so the specific limitations in one or more digital twin display device embodiments provided below may be referred to above for limitations of the digital twin display method, and will not be repeated here.

In one embodiment, as shown in fig. 16, there is provided a digital twin body display device comprising: an information acquisition module 1601, a model determination module 1602, a parameter determination module 1603, and a data transmission module 1604, wherein:

the information acquisition module 1601 is configured to acquire a target digital twin to be displayed from the stored digital twin according to a digital twin display request sent by the terminal, and determine a behavior type of an entity device corresponding to the target digital twin; the stored digital twin is constructed by a cloud server and issued to an edge server;

the model determining module 1602 is configured to determine a behavior model corresponding to the target digital twin according to the behavior type;

A parameter determining module 1603, configured to determine a target motion model corresponding to the behavior model, and obtain motion parameters required by the target motion model;

the data sending module 1604 is configured to return the motion parameter, the target motion model, and the target digital twin to the terminal, so that the terminal displays the analog motion of the target digital twin based on the motion parameter and the target motion model.

In one embodiment, the information obtaining module 1601 is further configured to obtain real-time monitoring information of the entity device; determining real-time state change information of the entity equipment according to the real-time monitoring information; and determining the behavior type of the entity equipment corresponding to the real-time state change according to the real-time state change information.

In one embodiment, the digital twin body display device further includes a real-time status module, configured to process the real-time monitoring information by using a status monitoring model deployed at the edge server, to obtain a real-time status result of the entity device; the state monitoring model is trained by the cloud server and issued to the edge server; and sending the real-time state result to the terminal, so that the terminal displays the real-time state result.

In one embodiment, the digital twin body display device further includes a prediction state module, configured to predict real-time monitoring information by using a state prediction model deployed at an edge server according to a state prediction request sent by a terminal, so as to obtain a predicted state result of the entity device; the state prediction model is trained by a cloud server and issued to an edge server; and returning the predicted state result to the terminal, so that the terminal displays the predicted state result.

In an embodiment, the prediction state module is further configured to send the real-time monitoring information and the prediction state result to the cloud server, so that the cloud server updates the state prediction model based on the real-time monitoring information and the prediction state result.

In one embodiment, as shown in fig. 17, another digital twin body display device is provided, comprising: a construction module 1701 and a transmission module 1702, wherein:

a building block 1701 for building a digital twin;

a sending module 1702 configured to issue the digital twin to an edge server; the edge server is used for acquiring a target digital twin body to be displayed from the received digital twin body according to a digital twin body display request sent by the terminal, determining the behavior type of entity equipment corresponding to the target digital twin body, determining a behavior model corresponding to the target digital twin body according to the behavior type, determining a target motion model corresponding to the behavior model, acquiring motion parameters required by the target motion model, and returning the motion parameters, the target motion model and the target digital twin body to the terminal so that the terminal displays the analog motion of the target digital twin body based on the motion parameters and the target motion model.

In one embodiment, a digital twin comprising a plurality of physical devices therein; the above construction module 1701 is further configured to obtain device information of each entity device; according to the equipment information, determining a first association relation between each entity equipment and the upper and lower level components and a second association relation between each level of components and the same level of components in each entity equipment; constructing a first association relation model of a first association relation by adopting an automatic mark language, and constructing a second association relation model of a second association relation by adopting a map database; and constructing a digital twin body of each entity device according to the first association relation model and the second association relation model.

The various modules in the digital twin display device described above may be implemented in whole or in part in software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.

In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 18. The computer device includes a processor, a memory, an Input/Output interface (I/O) and a communication interface. The processor, the memory and the input/output interface are connected through a system bus, and the communication interface is connected to the system bus through the input/output interface. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is for storing digital twin volume data. The input/output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used for communicating with an external terminal through a network connection. The computer program when executed by a processor implements a digital twin body exhibition method.

It will be appreciated by those skilled in the art that the structure shown in fig. 18 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application is applied, and that a particular computer device may include more or fewer components than shown, or may combine certain components, or have a different arrangement of components.

In an embodiment, there is also provided a computer device comprising a memory and a processor, the memory having stored therein a computer program, the processor implementing the steps of the method embodiments described above when the computer program is executed.

In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored which, when executed by a processor, carries out the steps of the method embodiments described above.

In an embodiment, a computer program product is provided, comprising a computer program which, when executed by a processor, implements the steps of the method embodiments described above.

It should be noted that, the user information (including, but not limited to, user equipment information, user personal information, etc.) and the data (including, but not limited to, data for analysis, stored data, presented data, etc.) referred to in the present application are information and data authorized by the user or sufficiently authorized by each party, and the collection, use and processing of the related data are required to comply with the related laws and regulations and standards of the related countries and regions.

Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, database, or other medium used in the various embodiments provided herein may include at least one of non-volatile and volatile memory. The nonvolatile Memory may include Read-Only Memory (ROM), magnetic tape, floppy disk, flash Memory, optical Memory, high density embedded nonvolatile Memory, resistive random access Memory (ReRAM), magnetic random access Memory (Magnetoresistive Random Access Memory, MRAM), ferroelectric Memory (Ferroelectric Random Access Memory, FRAM), phase change Memory (Phase Change Memory, PCM), graphene Memory, and the like. Volatile memory can include random access memory (Random Access Memory, RAM) or external cache memory, and the like. By way of illustration, and not limitation, RAM can be in the form of a variety of forms, such as static random access memory (Static Random Access Memory, SRAM) or dynamic random access memory (Dynamic Random Access Memory, DRAM), and the like. The databases referred to in the various embodiments provided herein may include at least one of relational databases and non-relational databases. The non-relational database may include, but is not limited to, a blockchain-based distributed database, and the like. The processors referred to in the embodiments provided herein may be general purpose processors, central processing units, graphics processors, digital signal processors, programmable logic units, quantum computing-based data processing logic units, etc., without being limited thereto.

The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.

The above examples only represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the present application. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application shall be subject to the appended claims.

Claims

1. A digital twin body display method, applied to an edge server, comprising:

2. The method of claim 1, wherein the determining the behavior type of the entity device to which the target digital twin corresponds comprises:

acquiring real-time monitoring information of the entity equipment;

3. The method of claim 2, further comprising, after obtaining the real-time monitoring information of the entity device:

4. The method of claim 2, further comprising, after obtaining the real-time monitoring information of the entity device:

5. The method of claim 4, further comprising, after obtaining the predicted state result of the entity device:

6. The digital twin body display method is characterized by being applied to a cloud server and comprising the following steps of:

Constructing a digital twin;

7. The method of claim 6, wherein the digital twins comprise digital twins of a plurality of physical devices;

the construction of a digital twin comprising:

acquiring equipment information of each entity equipment;

8. A digital twinning system, the system comprising: the edge server and the cloud server;

9. A digital twin body display device for use with an edge server, the device comprising:

10. A digital twin exhibiting device, for application to a cloud server, the device comprising:

A construction module for constructing a digital twin;

11. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor implements the steps of the method of any of claims 1 to 7 when the computer program is executed.

12. A computer readable storage medium, on which a computer program is stored, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.

13. A computer program product comprising a computer program, characterized in that the computer program, when being executed by a processor, implements the steps of the method of any of claims 1 to 7.