CN116466665A - Digital twin multi-protocol intelligent dispatching acquisition system and method for ship production workshop - Google Patents
Digital twin multi-protocol intelligent dispatching acquisition system and method for ship production workshop Download PDFInfo
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Abstract
The invention discloses a digital twin multi-protocol intelligent dispatching collection system and method for a ship production workshop, comprising workshop equipment, a multi-protocol data collection module, a digital twin virtual machine tool module and a data virtual-real combination visualization module, wherein the multi-protocol data collection module is connected with the workshop equipment to acquire actual production data of the workshop equipment in real time and interact with the digital twin virtual machine tool module; the digital twin virtual machine tool module is used for realizing intelligent production scheduling of workshop equipment and integrates entity data, environment data, sensor data and historical maintenance data; the data virtual-real combination visualization module is communicated with the multi-protocol data acquisition module and the digital twin virtual machine tool module through the Ethernet and is used for real data acquisition and analysis, data simulation mapping and three-dimensional scene display of workshop equipment. The invention improves the diversity of data acquisition protocols of production equipment of the cutting machine and the welding machine in the industrial ship production workshop.
Description
Technical Field
The invention belongs to the field of intelligent production technology and numerical control, and particularly relates to a digital twin multi-protocol intelligent scheduling and collecting system and method for a ship production workshop.
Background
The digital twinning is used as a novel intelligent manufacturing technology, through realizing virtual-real fusion between a physical product and a digital twinning body, the whole-course visualization of information is realized, the timeliness of the information and the timeliness of feedback are guaranteed, the digital twinning is widely used in the intelligent manufacturing field, the data acquisition is a foundation stone of the digital twinning, and whether the digital twinning building can be built to recover and carry forward and be stable depends on the integrity, the accuracy and the real-time property of acquired data. With the advent of various new technologies in the information age, data acquisition technologies have also been developed, and at present, data acquisition technologies have been applied in various fields, and are becoming the main stream of social development, including aerospace technologies, petroleum exploration, laboratory experiments, aerospace technologies, seismic monitoring, and the marine industry. Meanwhile, a ship production workshop is used as one of the current intelligent manufacturing core fields, and the problems that information in the machining process is blocked, machining feedback is not timely, and machining accuracy is difficult to guarantee are faced. Therefore, the digital twin technology, the digital acquisition technology and equipment in the industrial fields such as ship production workshops are combined to construct digital twin-based multi-acquisition equipment, and the digital twin-based multi-acquisition equipment has important effects on improving production quality, reducing production cost, avoiding processing risks, verifying rationality of a processing process and the like.
The digital twin multi-protocol ship production workshop collecting equipment is limited to the technical exploration in a certain aspect at present, and has the following defects:
1. the data flow of the acquisition equipment is complicated, the gateway does not have certain computing power, the feedback control of the equipment cannot be realized, only single acquisition can be realized, the acquisition of multiple protocols of the same equipment cannot be realized, the simultaneous acquisition of multiple protocols of multiple equipment cannot be realized, and the real-time monitoring is not realized;
2. in the existing scheme, the machine tool equipment has insufficient modeling and insufficient equipment data source, an inorganic bed action monitoring and early warning unit is not available, the system cannot perform intelligent collision early warning, an intelligent scheduling scheme cannot be automatically formed for production workshop products, and all product information cannot be displayed in real time in three dimensions; the digital twin model is not well established and has single function;
3. according to the workshop scheduling scheme of reinforcement learning, intelligent scheduling of workshop products with similar characteristics cannot be accurately and effectively performed; the method has no optimization program when the product is scheduled, and the screening comparison and the subsequent intelligent optimization of the intelligent scheme cannot be performed; the process of strengthening learning modeling always consumes time, is not suitable for a ship production workshop, and has unreasonable equipment utilization rate.
The research shows that the method has not been reported about multi-protocol digital acquisition, digital twin virtual machine tool modules of cutting machines and welding machine production equipment in ship production workshops, digital gateway communication, intelligent scheduling methods of digital twin ship production workshops and the like. Therefore, a digital twin multi-protocol intelligent dispatching acquisition system oriented to a ship production workshop is provided so as to solve the problems.
Disclosure of Invention
The invention aims to provide a digital twin multi-protocol intelligent dispatching collection system and method for a ship production workshop, which solve the problems of single collection data, delayed communication mode, single collection equipment, imperfect client interface function, single digital twin function, and behind manual experience dispatching, especially in transportation, loading and unloading and the like of the ship production workshop.
The technical solution for realizing the purpose of the invention is as follows:
the utility model provides a digital twin multiprotocol intelligent scheduling collection system towards boats and ships workshop, includes workshop equipment, multiprotocol data acquisition module, digital twin virtual machine tool module and data virtual-real combination visualization module, wherein:
the multi-protocol data acquisition module is connected with workshop equipment, acquires actual production data of the workshop equipment in real time, and interacts with the digital twin virtual machine tool module;
The digital twin virtual machine tool module is used for realizing intelligent production scheduling of workshop equipment, and integrates entity data, environment data, sensor data and historical maintenance data;
the data virtual-real combination visualization module is communicated with the multi-protocol data acquisition module and the digital twin virtual machine tool module through the Ethernet and is used for real data acquisition and analysis, data simulation mapping and three-dimensional scene display of workshop equipment;
the digital twin virtual machine tool module carries out intelligent production scheduling by receiving workshop equipment production data acquired by the multi-protocol data acquisition module in real time; and the digital twin virtual machine tool module performs data interaction with the data virtual-real combination visualization module, and performs screening comparison and intelligent optimization of an intelligent scheme in the data virtual-real combination visualization module to realize visual display.
Further, the workshop equipment comprises a cutting machine and welding machine production equipment; the multi-protocol data acquisition module is based on a multi-thread architecture and comprises a double-network-port transceiver module and a digital gateway module, and adopts various acquisition protocols including ModBus, fanuc, siemens, OPC UA and Rockwell acquisition protocols.
Further, the actual production data includes mechanical coordinates, absolute coordinates, relative coordinates, residual coordinates, feed speed, and status data of the apparatus.
Further, the intelligent production scheduling of the digital twin virtual machine tool module specifically includes:
establishing a machine tool PSN model;
constructing a similarity matrix, clustering similar attribute data in a feature layer sub-network by using the feature similarity matrix, and rapidly mapping information between features of the PSN model and a process-machine tool;
and determining a scheduling scheme by using the mapping relation of the super network.
Further, the establishing the machine tool PSN model specifically includes:
constructing a processing characteristic sub-network model, a processing technology layer sub-network model and a processing machine tool layer sub-network model;
determining mapping relations among heterogeneous nodes in the processing characteristic subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model;
and coupling the processing characteristic sub-network model, the processing technology layer sub-network model and the processing machine tool layer sub-network model through a sub-network layer coupling principle to obtain a PSN model, wherein each node in each sub-network layer is required to be contained in a set boundary according to the mapping relation among heterogeneous nodes, and the PSN super-network model with characteristic-process, characteristic-machine tool and process-machine tool coupling is obtained.
A digital twin multi-protocol intelligent scheduling and collecting method for a ship production workshop comprises the following steps:
S1, deploying equipment in a ship production workshop, and networking and connecting the equipment with a digital twin multi-protocol acquisition system;
s2, data acquisition is carried out on equipment in a ship production workshop by using a plurality of acquisition protocols of ModBus, fanuc, siemens, OPC UA and Luo Kewei mol respectively;
s3, the digital twin virtual machine tool module and the multi-protocol data acquisition module are used for carrying out mutual transmission of message queues in a digital network port mode, the digital twin virtual machine tool module is used for monitoring related data, and the data are stored in a local database in real time;
s4, a digital twin virtual machine tool module establishes a machine tool PSN model through feedback of equipment acquisition mapping, cutting mapping, welding mapping, motion prediction and a digital model in a ship production workshop, realizes bidirectional interaction of digital twin key data, and controls a multi-axis linkage mechanism of production equipment of a cutting machine and a welding machine to realize intelligent scheduling;
and S5, analyzing the PSN model by the data virtual-real combination visualization module, dynamically combining the PSN model with the real space scene, directly displaying the product scheduling data information on the product in real time, and directly controlling the product production information and the intelligent scheduling condition through the data virtual-real combination visualization screen control.
Compared with the prior art, the invention has the remarkable advantages that:
(1) The invention adopts a multi-protocol data acquisition mode, can be compatible with multiple protocols for data acquisition, provides more comprehensive and real data for digital twinning, is more flexible for the diversity of acquired data, and is suitable for equipment with different protocols;
(2) The digital twin virtual machine tool of the system can be used for realizing three-dimensional modeling display, collision prediction and intelligent production scheduling of production equipment of a cutting machine and a welding machine in a ship production workshop;
(3) Based on the digital twin lead-in super-network technology, the invention collects analysis data in a multi-physical modeling mode and efficiently establishes a machine tool production model such as PSN and the like; based on digital twin, the real-time production data are obtained through a cutting machine and welding machine production equipment in a ship production workshop, the digital gateway technology is used for communicating the ship production workshop with a digital twin system, and the production information and intelligent scheduling conditions of products can be controlled directly through the combination of data virtual reality and real reality with a visual screen control;
(4) The invention is developed based on multithreading architecture scheduling, is provided with double-network-port transceiving hardware and a digital gateway module, supports simultaneous data transceiving and supports simultaneous real-time acquisition of a plurality of devices.
Additional advantages, objects, and features of the invention will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following or may be learned from practice of the invention. The objectives and other advantages of the invention may be realized and attained by the structure particularly pointed out in the written description and drawings thereof as well as the appended drawings.
The invention is described in further detail below with reference to the accompanying drawings.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings, in which:
fig. 1 is a block diagram of a digital twin multi-protocol acquisition intelligent scheduling system of a ship production plant according to an embodiment of the present application.
Fig. 2 is a flowchart of a digital twin multi-protocol acquisition intelligent scheduling method in a ship production shop according to an embodiment of the present application.
Fig. 3 is a flow chart of cutting a ship production plant plate according to an embodiment of the present application.
Fig. 4 is a system multi-protocol acquisition diagram as shown in one embodiment of the present application.
FIG. 5 is a flow chart of a data acquisition process according to one embodiment of the present application.
FIG. 6 is a flow chart of the operation of the digital twin multi-protocol acquisition intelligent dispatch system of the marine vessel production plant according to one embodiment of the present application.
Detailed Description
Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. It should be understood that the preferred embodiments are presented by way of illustration only and not by way of limitation.
A digital twin multi-protocol intelligent scheduling and collecting system for a ship production workshop comprises: the system comprises a cutting machine, welding machine production equipment, a multi-protocol data acquisition module, a digital twin virtual machine tool module and a data virtual-real combination visualization module in a ship production workshop, wherein the multi-protocol data acquisition module is connected with the cutting machine and the welding machine production equipment in the ship production workshop and is used for acquiring actual production data in real time through the cutting machine and the welding machine production equipment in the ship production workshop, the ship production workshop is communicated with the digital twin system by using a digital gateway technology, and the system can be compatible with various protocols to acquire data and can transmit the data to the digital twin virtual machine tool module in real time;
the cutting machine and the welding machine production equipment in the ship production workshop can control the multi-shaft linkage mechanism to realize three-dimensional movement;
The multi-protocol data acquisition module performs data interaction with the data virtual-real combination visualization module in real time, so that dynamic visualization of data is realized.
The multi-protocol data acquisition module uses a digital gateway technology, can be compatible with various protocols for data acquisition, and performs information interaction between a ship production workshop and a digital twin system;
the data virtual-real combination visualization module performs real data acquisition analysis and data simulation mapping, and directly displays the intelligent scheduling information of the product in real time in a dynamic combination way with a real space scene;
further, the multi-protocol data acquisition module comprises a plurality of acquisition protocols including ModBus, fanuc, siemens, OPC UA and Luo Kewei mol.
The multi-protocol data acquisition module can acquire the mechanical coordinates, absolute coordinates, relative coordinates, residual coordinates, feeding rotation speed, state data and the like of the equipment in real time, meets the requirements of various acquired data of industrial equipment, also provides reliable data for digital twin intelligent scheduling modeling, and has the advantages of high efficiency, multiple acquisition protocol types and accurate acquired data.
Further, the client for data acquisition is developed based on multi-thread architecture scheduling. The industrial personal computer carrying the client is provided with a double-network-port transceiving hardware and a digital gateway module, supports simultaneous data transceiving and supports simultaneous real-time acquisition of a plurality of devices. The multi-protocol data acquisition module is connected with the cutting machine, the welding machine production equipment and the data virtual-real combination visualization module in the ship production workshop and is used for acquiring actual production data in real time through the cutting machine and the welding machine production equipment in the ship production workshop, the ship production workshop is communicated with the digital twin system by using a digital gateway technology, the multi-protocol data acquisition module can be compatible with various protocols for data acquisition, the data acquisition module can be used for transmitting the data acquisition data to the digital twin virtual machine tool module, and the data interaction is carried out with the data virtual-real combination visualization module in real time, so that the dynamic visualization of the data is realized.
Further, the digital twin virtual machine tool module models by receiving the production data of the ship workshop equipment acquired by the multi-protocol data acquisition module in real time; and the digital twin virtual machine tool module performs data interaction with the data virtual-real combination visualization module to realize the visualization display of three-dimensional modeling, collision prediction and intelligent scheduling.
Further, the digital twin virtual machine tool module realizes interactive integration of a physical workshop and a virtual workshop through a geometric model (a three-dimensional geometric model established by modeling software ensures consistency between three-dimensional details of the twin model and physical entities), a physical model (physical characteristics based on the geometric model, such as rotating speed of a cutting machine and hardness of a welding machine are added), a behavior model (an actual running path and motion constraint of the geometric model are added, so that the model can work in the same way as the physical entities), and an information model (the virtual model is ensured to read running data of the physical entities in real time, and real-time mapping between instructions and data information is realized).
Further, the digital twin virtual machine tool module has an actual processing time which is only a small part of the total processing time due to unreasonable scheduling plan of the existing production workshops. Considerable time is wasted in shipping, handling and waiting for processing. In order to improve production efficiency, the invention provides an intelligent scheduling method for a workshop by introducing a super network technology into a digital twin workshop.
The intelligent scheduling method comprises the following specific implementation steps:
1. because of the large amount of multidimensional, multi-relational heterogeneous data available in digital twin vessel production plants, efficient quantitative analysis is difficult. Therefore, firstly, a machine tool PSN model is established, and a platform is provided for centralized and classified management of various data types.
2. And clustering similar attribute data in the feature layer sub-network by using the feature similarity matrix, so that information between the features of the PSN and the process and the machine tool can be mapped quickly. The data preprocessing result shows that the similarity of each characteristic of the new part can be calculated, so that the similarity is matched with the similarity of the existing characteristic. The mapping relationship of the super network can be used to develop a scheduling scheme quickly. And verifying the rationality of the scheduling scheme on the basis of real-time simulation, thereby realizing the optimization characteristics of the virtual workshops and the intelligent scheduling of the workshops.
3. Finally, the proposed intelligent scheduling scheme is compared with a conventional scheduling method and the method is applied to a ship production plant.
Furthermore, the digital twin virtual machine tool module is used for improving the sense of reality of the ship workshop production equipment, collision body units are added to all parts of the ship workshop production equipment, the phenomenon that the parts of the virtual ship workshop production equipment pass through each other when in collision during operation is prevented, and intelligent collision early warning is carried out in the data virtual-real combination visualization module. The data virtual-real combination visualization module utilizes physical properties of collision bodies (Collider) endowed with different components (such as cutters and plates) in the Unity3D software to realize that after the cutters collide with the plates, the collision part of the plates, which is collided by the cutters, is hidden, namely, the animation effect of the plates after being cut by the cutters is realized, and intelligent collision early warning is carried out.
Further, the digital twin virtual machine tool module needs to give a specific action to the machine tool model after the ship production equipment model is successfully imported into the Unity3D software. In Unity3D, controlling different actions has different instructions. However, the principle is to give action frame number and change the position information of each frame of the appointed object so as to achieve the requirement of controlling different actions. In Unity3D, the operation setting for the machine tool model is completed by using a transfer component. The information of X, Y, Z axes of the selected model part (e.g. cutter assembly) can be given to the position (position) of the selected model part in the transfer assembly, so that the cutter can be moved from the current position to the designated position, and the moving picture of the cutter can be displayed. The rotation (rotation) command can give the cutter X, Y, Z different rotation angles to the shaft so as to realize the simulated cutting in different directions. And the data in the database is read in real time in the Unity3D platform, so that the motion gesture of the machine tool in the digital world and the physical world can be kept consistent, the online three-dimensional visualization of the machine tool in the digital world is realized, and the functions of virtual mapping and synchronous simulation are achieved.
Furthermore, the data virtual-real combination visualization module performs real data acquisition analysis and data simulation mapping to construct a three-dimensional visualization monitoring system of the ship production workshop, and the three-dimensional visualization monitoring system of the ship production workshop mainly comprises a physical subsystem and a virtual subsystem. The physical system comprises physical entities of production equipment such as a cutting machine and a welding machine, an integrated circuit board, a sensor, a digital interface and the like; the virtual system comprises production equipment such as a virtual cutting machine welder, a control panel, a parameter interface, production simulation and the like. The two are communicated in real time through a serial port and a communication protocol, and virtual and physical real-time mapping is realized.
A digital twin multi-protocol intelligent scheduling and collecting method for a ship production workshop specifically comprises the following steps:
s1, deploying a cutting machine and welding machine production equipment in a ship production workshop, and networking and connecting with a digital twin multi-protocol acquisition system;
s2, respectively carrying out data acquisition on cutting machines and welding machine production equipment in a ship production workshop by using a plurality of acquisition protocols of ModBus, fanuc, siemens, OPC UA and Luo Kewei mol;
and S3, the digital twin virtual machine tool module and the data acquisition device are mutually transmitted in a message queue in a digital network port mode, the digital twin mapping model is used for monitoring related data, and the data are stored in a local database in real time.
S4, the system establishes a machine tool PSN model through feedback of acquisition mapping, cutting mapping, welding mapping, motion prediction and digital models of cutting machines and welding machine production equipment in a ship production workshop, realizes bidirectional interaction of digital twin key data, and controls multi-axis linkage mechanisms of the cutting machines and the welding machine production equipment to realize intelligent scheduling.
And S5, finally, analyzing the digital twin simulation model by combining the virtual and the actual data with a visual interface, and dynamically combining the digital twin simulation model with a real space scene to directly display the product scheduling data information on the product in real time, wherein the product production information and the intelligent scheduling condition can be controlled by directly combining the virtual and the actual data with a visual screen control.
The method is based on the system and comprises all technical features of the system, which are not described in detail here.
Examples
The embodiment provides a digital twin multi-protocol intelligent dispatching collection system for a ship production workshop. The problems of single data acquisition, delayed communication mode, single acquisition equipment, imperfect client interface function, single digital twin function, lagged manual experience dispatching, transportation, loading and unloading and the like of a ship production workshop are solved, and novel technical means such as multi-protocol digital acquisition of the ship production workshop, digital twin virtual machine tool system of the ship production workshop, intelligent dispatching mode and the like are adopted to solve the problems.
FIG. 1 is a block diagram of a digital twin multi-protocol acquisition system of a marine vessel production plant according to one embodiment of the present application. In some embodiments, mainly comprising: the system comprises a cutting machine, welding machine production equipment, a multi-protocol data acquisition module, a digital twin virtual machine tool module and a data virtual-real combination visualization module in a ship production workshop. The multi-protocol data acquisition module is connected with the cutting machine and the welding machine production equipment in the ship production workshop and is used for acquiring actual production data in real time through the cutting machine and the welding machine production equipment in the ship production workshop, the ship production workshop is communicated with the digital twin system by using a digital gateway technology, and the multi-protocol data acquisition module can be compatible with various protocols for data acquisition and can transmit the data to the digital twin virtual machine tool module; the production equipment of the cutting machine and the welding machine in the ship production workshop comprises a plurality of production equipment such as a numerical control cutting machine, a welding machine and the like, is controlled by a numerical control system, can control a multi-axis linkage mechanism to realize three-dimensional movement, and is provided with communication interfaces with different protocols, and working parameters including position and speed are externally output; the data virtual-real combination visualization module is used for displaying a three-dimensional scene of workshop equipment according to the digital twin mapping data, predicting workpiece motion, detecting collision and directly and dynamically displaying processing process data information in real time in combination with a real space; the digital twin virtual machine tool module is used for realizing acquisition mapping, cutting mapping, welding mapping, motion prediction, collision detection and intelligent production scheduling of cutting machines and welding machine production equipment in a ship production workshop, integrates entity data, environment data, sensor data and historical maintenance data and related derivative data generated by excavation, constructs a virtual machine tool model, carries out simulation and simulation of a processing process, can be displayed in three dimensions in real time, further introduces a super network technology into the ship production workshop, and can realize optimal scheduling of a processing process of a ship middleware, so that the system is more efficient.
The production equipment of the cutting machine and the welding machine in the ship production workshop can control the multi-axis linkage mechanism through the digital twin system to realize three-dimensional movement.
The multi-protocol data acquisition module comprises a plurality of acquisition protocols including ModBus, fanuc, siemens, OPC UA and Luo Kewei mol, can acquire the mechanical coordinates, absolute coordinates, relative coordinates, residual coordinates, feeding rotation speed and state data of the equipment in real time, meets the requirements of various acquisition data of industrial equipment, and has the advantages of high efficiency, multiple acquisition protocol types and accurate acquisition data.
The multi-protocol data acquisition module uses a digital gateway technology, can be compatible with various protocols for data acquisition, and performs information interaction between a ship production workshop and a digital twin system.
The clients of the multiprotocol data acquisition module for data acquisition are developed based on multithreaded architecture scheduling. The industrial personal computer carrying the client is provided with a double-network-port transceiving hardware and a digital gateway module, supports simultaneous data transceiving and supports simultaneous real-time acquisition of a plurality of devices.
And the data virtual-real combination visualization module performs real data acquisition analysis and data simulation mapping, and directly displays the intelligent scheduling information of the product in real time in combination with the dynamic state of the real space scene.
The digital twin virtual machine tool module is used for realizing acquisition mapping, cutting mapping, welding mapping, motion collision prediction and intelligent production scheduling of various equipment of a cutting machine and welding machine production equipment in a ship production workshop, integrates entity data, environment data and historical maintenance data and related derivative data generated by excavation, constructs a virtual machine tool model, carries out simulation and simulation of a processing process, can be displayed in three-dimensional real time, further introduces a super-network technology into the ship production workshop, and provides an intelligent scheduling method aiming at the ship production workshop, which can realize optimal scheduling of the processing process of a ship middleware, so that the system is more efficient and convenient.
FIG. 2 is a flow chart of a digital twin multi-protocol acquisition intelligent scheduling method for a marine vessel production plant according to one embodiment of the present application. The implementation process of the whole intelligent scheduling scheme is divided into the following steps: establishing a digital twin virtual machine tool module, establishing a PSN model, establishing a similarity matrix, calculating and matching characteristic similarity, calculating mapping and simulating, and producing an intelligent scheduling scheme. The respective modules thereof function specifically as follows.
The digital twin virtual machine tool module can be used for realizing three-dimensional modeling display, collision prediction and intelligent production scheduling of production equipment of a cutting machine and a welding machine in a ship production workshop.
The digital twin virtual machine tool module realizes three-dimensional modeling. After the ship production facility model is successfully imported into the Unity3D software, a specific action needs to be given to the machine tool model. In Unity3D, controlling different actions has different instructions. However, the principle is to give action frame number and change the position information of each frame of the appointed object so as to achieve the requirement of controlling different actions. In Unity3D, the operation setting for the machine tool model is completed by using a transfer component. The information about the axis of the cutter assembly X, Y, Z in the transfer assembly is given, and the movement image display of the cutter can be realized by using a transfer position=vector 3. Movement tools (transfer position, air position, speed time). The rotation command transform (new Vector3 (0, -speed time) gives the cutter X, Y, Z different rotation angles to achieve the simulated cutting in different directions. And the data in the database is read in real time in the Unity3D platform, so that the motion gesture of the machine tool in the digital world and the physical world can be kept consistent, the online three-dimensional visualization of the machine tool in the digital world is realized, and the functions of virtual mapping and synchronous simulation are achieved. Furthermore, the digital twin virtual machine tool module is a sense of reality for lifting the ship workshop production equipment, collision body units are added to all parts of the ship workshop production equipment, and the phenomenon that the parts of the virtual ship workshop production equipment pass through each other when in collision in operation is prevented. In the Unity3D software, physical properties of collision bodies (such as cutters and plates) of different components are endowed, namely, the collision detection of the cutters and the plates can be realized through OnTriggerEnter (Collider plate) commands, and then the collision part of the plates, which is collided by the cutters, is destroyed through the destroyer commands, so that the animation effect of the plates after being cut by the cutters can be realized. The specific flow is shown in fig. 3.
The digital twin system adopts multiple physical modeling to realize multiple physical modeling functions of digital twin, an intelligent scheduling model is mainly constructed through the twin system, the digital twin is truly reflected in a virtual space by a physical entity, the success degree of the digital twin applied in the industrial field depends on the fidelity degree of the digital twin, namely the simulation degree, so the multiple physical modeling improves the simulation degree of the digital twin and fully exerts the digital twin; the digital twin system realizes the collection mapping, the cutting mapping, the welding mapping, the motion prediction and the feedback of the digital model of the cutting machine and the welding machine production equipment in the ship production workshop, and can integrally realize the real-time dynamic model establishment and the monitoring feedback of the cutting machine and the welding machine production equipment in the ship production workshop.
The digital twin virtual machine tool module realizes collision prediction. In order to improve the sense of reality of the ship workshop production equipment, collision body units are added to all parts of the ship workshop production equipment, and the phenomenon that the parts of the virtual ship workshop production equipment pass through each other when in collision during operation is prevented. The collision bodies in the Unity3D software are divided into 6 types, different collision body types can be added according to the shape and the application of machine tool parts, and the prediction of the collision of the cutting machine and welding machine production equipment in a ship production workshop is realized.
The digital twin virtual machine tool module realizes intelligent scheduling. The digital twin virtual machine tool module is combined with the multi-protocol data acquisition module, various data are acquired and interacted with the digital twin virtual machine tool module in real time, and further interaction between a virtual space and a real space can be achieved, new models such as a production machine tool PSN and the like are established, a platform is provided for centralized and classified management of various data types, and intelligent scheduling of a ship workshop is achieved.
The implementation process of the intelligent scheduling scheme comprises the following steps: and constructing a characteristic layer super network similarity matrix. In comparing the similarity of the features of two production facilities, the following six attributes cannot be directly calculated, and a similarity matrix must be constructed to calculate the similarity between functions. The main decision attribute characteristics of the processing of the ship production workshop comprise structure types, materials, categories, precision, size and roughness, the mathematical model of the processing characteristics is PP= { PC, PM, S, PS, MA, PR } wherein PC is a process category, such as a cutting machine and a welding machine; PM is a work material, such as stainless steel, cast iron; ST is a structural type such as a center hole, a pinhole, etc.; PS is the process size; MA is the precision, e.g., cut material sheet thickness; PR refers to the close distribution of peaks and valleys of process roughness due to the fine geometric features of the machined surface, affecting quality of service workpiece performance and service life. The six attributes of the process feature belong to different data types, namely PC, PM, ST belong to qualitative symbol data, and PS, MA, PR belong to quantitative symbol data. In comparing the similarity of two features, a similarity matrix is constructed to calculate the similarity between functions. The construction is as follows:
Two process features of attribute f may be defined as (f i ,f j ) And the similarity between the two features is as follows:
wherein alpha, beta, gamma are weight coefficients; p, S, C are the distance factor, span distance factor and content distance factor respectively for the position; and α+β+γ=1, α ε [0,1], β ε [0,1], γ ε [0,1].
(1) When f i And f j Is quantitative data, and their similarity can be calculated as the distance of two values at a position, the spatial span and the content: the position distance factor P (f i ,f j ) The method comprises the following steps:
space span distance factor S (f i ,f j ) The method comprises the following steps:
content distance factor C (f) i ,f j ) The method comprises the following steps:
f i (min) is f i Lower limit of f i (max) is f i Upper limit of f j (min) is f j Lower limit of f j (max) is f j Upper limit of (2), inter is f i And f j Number of intersection values of U f Is the difference between the maximum and minimum of the f-th attribute.
(2) When f i And f j For qualitative data, position and U in distance calculation f The items are fewer:
space span distance factor S (f i ,f j ) The method comprises the following steps:
content distance factor C (f) i ,f j ) The method comprises the following steps:
m i is f i Number m of elements j Is f j Wherein m is the number of elements in f Is f i And f j The total number of elements in (1), the inter is f i And f j Number of intersecting values.
According to the similarity calculation process, a similarity matrix between the features is obtained as follows:
according to different characteristic attributes, the characteristics can be clustered into different classes, so that the smaller the difference between the classes is, the better the similarity in the classes is. The cluster mapping point set is reduced from a high-dimensional space to a low-dimensional space, and the network dimension is reduced while the original distribution of the data is maintained. In addition, clustering the super-network layer data assigns the data to different classes and provides a basis for correlation to establish a scheduling scheme in the feature-machine tool super-network.
The implementation process of the intelligent scheduling scheme comprises the following steps: and (6) establishing a new model. In order to quantitatively study the relation among the multisource data of the digital twin workshop 'characteristic process machine tool', a PSN model is built on the basis of a stored workshop production process database.
The PSN model is established by the following specific steps and modes:
the psn consists of three layers of sub-network sets and boundary sets, as follows:
wherein N is PF To process feature layer sub-networks, N PP For processing the process layer sub-network N PM Is a sub-network of machine tool layers. E (E) PF-PP Is N PF And N PP Intersection between E PF-PM Is N PF And N PM Intersection between E PP-PM Is N PP And N PM And a set of boundaries between.
2. Processing feature subnetwork (N) PF ) The model is as follows:
wherein v is pf-n Is N PF And (v) pf-i ,v pf-j ) Is N PF V of the inner part pf-i And v pf-j Boundary sums of (a) are provided.
3. Processing technology layer subnet (N) PP ) Is defined by:
the processing technology layer sub-network model is as follows:
wherein v is pp-n Is N PP And (v) pp-x ,v pp-y ) Is N PP V of the inner part pp-x And v pp-y Boundary sums of (a) are provided.
4. Processing machine layer subnet (N) PM ) Definition of (2)
The machine tool subnetwork model is as follows:
wherein v is pm-n Is N PM And (v) pm-α ,v pm-β ) Is N PM V of the inner part pm-α And v pm-β Boundary sums of (a) are provided.
5. Mapping relationship between heterogeneous nodes
(1)N PF And N PP Mapping relation between
The mapping relationship between a property and a process reflects the property that the process may contain, or the flow to which the property may belong. Let boolean variable θ (v) pf-i ,v pp-x ) Representing characteristic v pf-i Sum procedure v pp-x The relationship between them is as follows
Thus, the mapping relationship between features and processes can be defined by the above equation. From the mapping of processes to features, it is known which process corresponds to which feature.
At the characteristic point set V PF Procedure v pp-x Comprising a set of feature points as follows:
V PF (v pp-x )＝f(v pp-x ,V PF )＝{v pf-i |v pf-i ∈V PF ,θ(v pf-i ,v pp-x )＝1}
this represents the characteristic V PF (v pp-x ) Corresponding v pp-x Is a process of (2).
From feature to procedure V PP Can determine which processes correspond to those features v pf-i 。
V PP (v pf-i )＝f(v pf-i ,V PP )＝{v pp-x |v pp-x ∈V PP ,θ(v pf-i ,v pp-x )＝1}
This represents procedure V PP (v pf-i ) Corresponding v pf-i Features.
(2)N PP And N PM Mapping relation between
The mapping between the process and the machine tools is based on which machine tools can be used to complete a process, or which process can be completed with the machine tools. Let the Boolean variable θ (v pp-x ,v pm-α ) Representation procedure v pp-x And machine tool v pm-α The relationship between them is as follows:
thus, the mapping relationship between the machine tool and the process can be defined by the above equation.
From the process-to-machine mapping, it is known which machine is associated with a certain process.
Machine tool V PM And corresponding procedure v pp-x The relationship of (2) is as follows:
V PM (v pp-x )＝f(v pp-x ,V PM )＝{v pm-α |v pm-α ∈V PM ,θ(v pm-α ,v pp-x )＝1}
V PM (v pp-x ) Indicating that the machine tool corresponds to process v pp-x
Procedure V PP The relation with the corresponding machine tool is as follows:
V PP (v pm-α )＝f(v pm-α ,V PP )＝{v pp-x |v pp-x ∈V PP ,θ(v pm-α ,v pp-x )＝1}
V PP (v pm-α ) The representation corresponds to machine tool v pm-α
6. The subnetworks are coupled. By constructing N PF ，N PP ,N PM PSN can be obtained by a subnet layer coupling principle. Each node in each subnet layer must be included at the set boundary according to the mapping relationship between heterogeneous nodes of the PSN. Likewise, a super network with feature-process, feature-machine, process-machine coupling can be obtained.
The feature-process super network is defined as follows:
φ PF-PP is N PF And N PP Set of couplings between, SE PF-PP Is N PF Point v in pf-i And N PP Point v in pp-x A set of boundaries between.
The feature-machine super network is defined as follows:
φ PF-PM is N PF And N PM Set of couplings between, SE PF-PM Is N PF Point v in pf-i And N PM Point v in pm-α A set of boundaries between.
The process-machine tool super network is defined as follows:
φ PP-PM is N PP And N PM Set of couplings between, SE PP-PM Is N PP Point v in pp-x And N PM Point v in pm-α A set of boundaries between.
And (3) comprehensively integrating and mapping the data of the characteristic processing-machine tool according to the PSN model constructed above.
Specifically, researchers find out corresponding processes and machine tools according to the processing characteristics of parts to be processed by utilizing boundary connection in a super network, and effective technical support is provided for realizing intelligent scheduling of ship production workshops.
In combination with the innovation, the intelligent scheduling method of the system has the following application processes:
1. geometric model: and establishing a three-dimensional geometric model by using modeling software according to a design drawing of a production unit of a ship production workshop. The goal of this process is to ensure consistency between the three-dimensional details of the twin model and the physical entity.
2. Physical model: the information of physical characteristics based on a geometric model, such as cutter speed, material size, welding machine temperature materials and the like, is added.
3. Behavior model: on the basis of the physical model, the actual running path and motion constraint of the geometric model are added, so that the model can work in the same way as the physical entity.
4. Information model: using a unified communications protocol, the information of the "physical plant-server-virtual plant" can be interconnected. Then, the virtual model can read the operation data of the physical entity in real time, and real-time mapping between the instruction and the data information is realized.
5. Through the four-level functional cooperation, the interaction integration of the physical workshop and the virtual workshop is realized, and the digital twin three-dimensional modeling is completed.
6. The PSN model is then built on the basis of a stored database of ship shop production processes (the model building process is described in detail above). The processing features (feature processing utilizes a feature matrix, which is described in detail above in the modeling of the feature matrix) are used as the minimum entry points for production scheduling, and historical process data and real-time data in the ship production shop process are refined into different super-network layers. In order to effectively match the discrete features of the new portion, similar attribute features in the feature layer subnetwork are clustered using a similarity matrix. The purpose of the matrix is to compute the similarity of the processing features of each ship machine, such as a cutter, a welder, etc., and then match the features in the corresponding database. The type and position of the machine tool corresponding to the feature can be determined by the mapping relation of the super network.
7. Based on this, the processing time, the transfer time, and the waiting time between adjacent features (the first step is to determine whether or not there is waiting time for the workpiece) of the feature can be further determined, and the time for processing the corresponding feature can be determined by calculation and matching of the feature similarity. And determining the transfer time of the workpiece between adjacent features according to the processing time of the workpiece, and further performing intelligent scheduling.
Fig. 4 is a multi-protocol acquisition diagram of a cutter, welder production facility in a marine production plant according to one embodiment of the present application. As shown in fig. 4, the compatible protocols of the digital twin systems of the cutting machine and the welding machine production equipment in the ship production workshop of the design comprise ModBus, fanuc, siemens, OPC UA and Luo Kewei mol, and various acquisition protocols can be operated in parallel.
Wherein the ModBus comprises a ModBus Tcp. Wherein Siemens comprises S7-S1200 and S7-S1500.
Wherein Mitsubishi PLC includes EtherNet/IP (CIP). Wherein the Rockwell comprises EtherNet/IP (CIP).
FIG. 5 is a flow chart of a data acquisition process according to one embodiment of the present application. As shown in fig. 5, the acquisition process mainly comprises the following steps:
step 1: running client software;
Step 2: selecting equipment to be acquired from an equipment library, configuring related communication addresses, clicking test connection to monitor whether normal connection is possible, and clicking and storing the equipment to the added equipment library;
step 3: if a plurality of devices are needed to be added, repeating the step 2;
step 4: after the equipment is added, unified collection is carried out, a unified connection button on a client is clicked to carry out equipment matching connection, then the collection is clicked again, background data collection is carried out, the background data are automatically stored in a database, and related data are output to a related module;
the background is collected uniformly, and meanwhile, a certain device can be double-clicked to carry out relevant monitoring, and a monitoring interface is displayed at the moment;
the collection is finished or the temporary interruption is needed to be carried out, the collection can be uniformly finished, and the collection can be carried out only in a single machine when needed;
and after the collection task is finished and is not continued, the collection task is finished, and then the connection is disconnected uniformly.
FIG. 6 is a flow chart of the operation of the digital twin multi-protocol acquisition intelligent scheduling system of the marine vessel production plant according to one embodiment of the present application. As shown in fig. 6, the operation of the production equipment of the cutting machine and the welding machine in the ship production workshop is mainly carried out by the following steps:
1) The production equipment of the cutting machine and the welding machine in the ship production workshop starts to operate;
2) The acquisition module reads various types of required data, and analyzes special data such as coordinate data, current voltage data and the like to the appointed module;
3) And the digital twin virtual machine tool module acquires data, establishes a simulation mapping model PSN, and outputs the data for virtual and real visual monitoring.
4) The digital twin virtual machine tool module is used for carrying out simulation after being mapped with the model, judging a scheduling scheme which is expected to operate, displaying the scheduling scheme on a visual interface combining data virtual and real in real time, and carrying out corresponding feedback operation through the interface.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (10)
1. The digital twin multi-protocol intelligent scheduling and collecting system for the ship production workshop is characterized by comprising workshop equipment, a multi-protocol data collecting module, a digital twin virtual machine tool module and a data virtual-real combination visualization module, wherein:
The multi-protocol data acquisition module is connected with workshop equipment, acquires actual production data of the workshop equipment in real time, and interacts with the digital twin virtual machine tool module;
the digital twin virtual machine tool module is used for realizing intelligent production scheduling of workshop equipment, and integrates entity data, environment data, sensor data and historical maintenance data;
the data virtual-real combination visualization module is communicated with the multi-protocol data acquisition module and the digital twin virtual machine tool module through the Ethernet and is used for real data acquisition and analysis, data simulation mapping and three-dimensional scene display of workshop equipment;
the digital twin virtual machine tool module carries out intelligent production scheduling by receiving workshop equipment production data acquired by the multi-protocol data acquisition module in real time; and the digital twin virtual machine tool module performs data interaction with the data virtual-real combination visualization module, and performs screening comparison and intelligent optimization of an intelligent scheme in the data virtual-real combination visualization module to realize visual display.
2. The digital twin multi-protocol intelligent scheduling and collecting system for ship production workshops according to claim 1, wherein the workshop equipment comprises a cutting machine and welding machine production equipment; the multi-protocol data acquisition module is based on a multi-thread architecture and comprises a double-network-port transceiver module and a digital gateway module, and adopts various acquisition protocols including ModBus, fanuc, siemens, OPC UA and Rockwell acquisition protocols.
3. The ship production shop oriented digital twin multi-protocol intelligent scheduling and collecting system according to claim 1, wherein the actual production data comprises mechanical coordinates, absolute coordinates, relative coordinates, residual coordinates, feeding rotation speed and state data of equipment, and the digital twin virtual machine tool module displays all product information in real time and three-dimensionally.
4. The digital twin multi-protocol intelligent scheduling acquisition system for a ship production shop according to claim 1, wherein the digital twin virtual machine tool module performs intelligent production scheduling specifically comprises:
establishing a machine tool PSN model;
constructing a similarity matrix, clustering similar attribute data in a feature layer sub-network by using the feature similarity matrix, and rapidly mapping information between features of the PSN model and a process-machine tool;
and determining a scheduling scheme by using the mapping relation of the super network.
5. The digital twin multi-protocol intelligent scheduling and collecting system for ship production workshops according to claim 1, wherein the establishing a machine tool PSN model specifically comprises:
constructing a processing characteristic sub-network model, a processing technology layer sub-network model and a processing machine tool layer sub-network model;
Determining mapping relations among heterogeneous nodes in the processing characteristic subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model;
and coupling the processing characteristic sub-network model, the processing technology layer sub-network model and the processing machine tool layer sub-network model through a sub-network layer coupling principle to obtain a PSN model, wherein each node in each sub-network layer is required to be contained in a set boundary according to the mapping relation among heterogeneous nodes, and the PSN super-network model with characteristic-process, characteristic-machine tool and process-machine tool coupling is obtained.
6. The digital twin multi-protocol intelligent dispatch collection system for a ship production shop of claim 1, wherein the processing feature subnetwork model is:
wherein v is pf-n Is a processing characteristic subnetwork model N PF And (v) pf-i ,v pf-j ) Is N PF V of the inner part pf-i And v pf-j Boundary sums of (2);
the processing technology layer subnet model is as follows:
the processing technology layer sub-network model is as follows:
wherein v is pp-n Is a processing technology layer subnet model N PP And (v) pp-x ,v pp-y ) Is N PP V of the inner part pp-x And v pp-y Boundary sums of (2);
the machine tool layer sub-network model is as follows:
wherein v is pm-n Is a machine tool layer sub-network model N PM Is the first of (2)n nodes, and (v) pm-α ,v pm-β ) Is N PM V of the inner part pm-α And v pm-β Boundary sums of (a) are provided.
7. The digital twin multi-protocol intelligent scheduling and collecting system for ship production workshops according to claim 7, wherein the determining the mapping relationship among the heterogeneous nodes in the processing characteristic subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model specifically comprises:
(1)N PF and N PP Mapping relation between
Let boolean variable θ (v) pf-i ,v pp-x ) Representing characteristic v pf-i Sum procedure v pp-x The relation between the two is:
at the characteristic point set V PF Procedure v pp-x The method comprises the following characteristic point sets:
V PF (v pp-x )＝f(v pp-x ,V PF )＝{v pf-i |v pf-i ∈V PF ,θ(v pf-i ,v pp-x )＝1}
this represents the characteristic V PF (v pp-x ) Corresponding v pp-x Process V of (a) PP (v pf-i ) Corresponding v pf-i Is characterized in that:
V PP (v pf-i )＝f(v pf-i ,V PP )＝{v pp-x |v pp-x ∈V PP ,θ(v pf-i ,v pp-x )＝1}
(2)N PP and N PM Mapping relation between
Another boolean variable θ (v) pp-x ,v pm-α ) Representation procedure v pp-x And machine tool v pm-α Relationship between:
machine tool V PM And corresponding procedure v pp-x Relation V of (2) PM (v pp-x ) The method comprises the following steps:
V PM (v pp-x )＝f(v pp-x ,V PM )＝{v pm-α |v pm-α ∈V PM ,θ(v pm-α ,v pp-x )＝1}
procedure V PP And corresponding machine tool v pm-α The relation of (2) is:
V PP (v pm-α )＝f(v pm-α ,V PP )＝{v pp-x |v pp-x ∈V PP ,θ(v pm-α ,v pp-x )＝1}。
8. the digital twin multi-protocol intelligent dispatch collection system for a ship production shop of claim 7, wherein the PSN super network model with feature-process, feature-machine, process-machine coupling specifically comprises:
the feature-process super network is:
φ PF-PP is N PF And N PP Set of couplings between, SE PF-PP Is N PF Point v in pf-i And N PP Point v in pp-x A set of boundaries between;
the characteristic-machine tool super network is as follows:
φ PF-PM is N PF And N PM Set of couplings between, SE PF-PM Is N PF Point v in pf-i And N PM Point v in pm-α A set of boundaries between;
the process-machine tool super network is:
φ PP-PM is N PP And N PM Set of couplings between, SE PP-PM Is N PP Point v in pp-x And N PM Point v in pm-α A set of boundaries between.
9. The digital twin multi-protocol intelligent scheduling and collecting system for the ship production workshop of claim 7, wherein the digital twin virtual machine tool module adds collision body units to all parts of the ship production workshop production equipment, and intelligent collision early warning is carried out in the data virtual-real combination visualization module.
10. The digital twin multi-protocol intelligent scheduling and collecting method for the ship production workshop is characterized by comprising the following steps of:
s1, deploying equipment in a ship production workshop, and networking and connecting the equipment with a digital twin multi-protocol acquisition system;
s2, data acquisition is carried out on equipment in a ship production workshop by using a plurality of acquisition protocols of ModBus, fanuc, siemens, OPC UA and Luo Kewei mol respectively;
s3, the digital twin virtual machine tool module and the multi-protocol data acquisition module are used for carrying out mutual transmission of message queues in a digital network port mode, the digital twin virtual machine tool module is used for monitoring related data, and the data are stored in a local database in real time;
S4, a digital twin virtual machine tool module establishes a machine tool PSN model through feedback of equipment acquisition mapping, cutting mapping, welding mapping, motion prediction and a digital model in a ship production workshop, realizes bidirectional interaction of digital twin key data, and controls a multi-axis linkage mechanism of production equipment of a cutting machine and a welding machine to realize intelligent scheduling;
and S5, analyzing the PSN model by the data virtual-real combination visualization module, dynamically combining the PSN model with the real space scene, directly displaying the product scheduling data information on the product in real time, and directly controlling the product production information and the intelligent scheduling condition through the data virtual-real combination visualization screen control.
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