CN116466665A - Digital twin multi-protocol intelligent dispatching acquisition system and method for ship production workshop - Google Patents

Abstract

The invention discloses a digital twin multi-protocol intelligent scheduling and acquisition system and method for ship production workshops, including workshop equipment, multi-protocol data acquisition modules, digital twin virtual machine tool modules and virtual and real data combination visualization modules, the multi-protocol data acquisition module is connected with workshop equipment, real-time acquisition of actual production data of workshop equipment, and interacts with digital twin virtual machine tool modules at the same time; digital twin virtual machine tool modules are used to realize intelligent production scheduling of workshop equipment, which integrates entity data, environment data, sensor data and historical maintenance data; data virtual reality combined visualization module through Ethernet and multi-protocol data acquisition module , Digital twin virtual machine tool module communication, used for real data acquisition and analysis, data simulation mapping and display of three-dimensional scenes of workshop equipment. The invention improves the diversity of the data acquisition protocols of the cutting machine and welding machine production equipment in the industrial ship production workshop.

Description

Digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshop and method

technical field

The invention belongs to the fields of intelligent production technology and numerical control, and in particular relates to a digital twin multi-protocol intelligent scheduling and acquisition system and method for ship production workshops.

Background technique

As a new type of intelligent manufacturing technology, digital twin is widely used in the field of intelligent manufacturing by realizing the integration of virtual and real between physical products and digital twins, realizing the whole process of information visualization, ensuring the timeliness of information and the timeliness of feedback, and data collection is the cornerstone of digital twins. With the birth of various new technologies in the information age, data acquisition technology has also been developed. At present, data acquisition technology has been applied in various fields, and has become the mainstream of social development, including aerospace technology, oil exploration, laboratory tests, aerospace technology, earthquake monitoring, shipbuilding industry, etc. At the same time, the ship production workshop, as one of the core areas of intelligent manufacturing at present, is facing the problem of blocked processing information, untimely processing feedback, and difficult to guarantee processing accuracy. Therefore, combining digital twin technology, digital acquisition technology with industrial equipment such as ship production workshops to create multi-acquisition equipment based on digital twins will play an important role in improving production quality, reducing production costs, avoiding processing risks, and verifying the rationality of the processing process.

At present, the multi-protocol ship production workshop acquisition equipment related to digital twins is mostly limited to a certain aspect of technical research, and has the following shortcomings:

1. The process of collecting device data is cumbersome, the gateway does not have certain computing power, cannot perform feedback control on the device, can only realize single collection, cannot collect multiple protocols of the same device, and collect multiple protocols of multiple devices at the same time without simulation for real-time monitoring;

2. In the current plan, there are insufficient machine tool equipment modeling, insufficient equipment data sources, no machine tool movement monitoring and early warning unit, the system cannot perform intelligent collision early warning, cannot automatically form an intelligent scheduling plan for the products in the production workshop, and cannot display all product information in real time in three dimensions; and the establishment of the digital twin model is not perfect, and the function is single;

3. The workshop scheduling plan based on reinforcement learning cannot accurately and effectively intelligently schedule workshop products with similar characteristics; and there is no optimization program during product scheduling, and the screening and comparison of intelligent solutions and subsequent intelligent optimization cannot be carried out; in the process of reinforcement learning modeling, it always takes time and is not suitable for ship production workshops, and the utilization rate of equipment is unreasonable.

After searching, it is found that there are no reports on multi-protocol digital acquisition, digital twin virtual machine tool modules for cutting machines and welding machine production equipment in ship production workshops, digital gateway communication, and intelligent scheduling methods for digital twin ship production workshops. Therefore, a digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops is proposed to solve the problems raised above.

Contents of the invention

The purpose of the present invention is to provide a digital twin multi-protocol intelligent scheduling and acquisition system and method for ship production workshops to solve the problems of single data collection, communication mode delay, single acquisition equipment, insufficient client interface functions, single digital twin functions, and backward manual experience scheduling in ship production workshops, especially in transportation, loading and unloading.

The technical solution that realizes the object of the present invention is:

A digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops, including workshop equipment, multi-protocol data acquisition modules, digital twin virtual machine tool modules, and virtual-real data combined visualization modules, in which:

The multi-protocol data acquisition module is connected to the workshop equipment to obtain the actual production data of the workshop equipment in real time, and interact with the digital twin virtual machine tool module at the same time;

The digital twin virtual machine tool module is used to realize intelligent production scheduling of workshop equipment, which integrates entity data, environmental data, sensor data and historical maintenance data;

The virtual and real data combination visualization module communicates with the multi-protocol data acquisition module and the digital twin virtual machine tool module through Ethernet, and is used for real data acquisition and analysis, data simulation mapping and display of three-dimensional scenes of workshop equipment;

The digital twin virtual machine tool module performs intelligent production scheduling by receiving the production data of workshop equipment collected by the multi-protocol data acquisition module in real time; the digital twin virtual machine tool module then performs data interaction with the data virtual and real combination visualization module, and performs screening and comparison of intelligent solutions and intelligent optimization in the data virtual and real combination visualization module to realize visual display.

Further, the workshop equipment includes cutting machines and welding machine production equipment; the multi-protocol data acquisition module is based on a multi-threaded architecture, includes a dual-network port transceiver module and a digital gateway module, and adopts a variety of acquisition protocols, including ModBus, Fanuc, Siemens, OPC UA and Rockwell acquisition protocols.

Further, the actual production data includes mechanical coordinates, absolute coordinates, relative coordinates, remaining coordinates, feed speed and status data of the equipment.

Further, the intelligent production scheduling of the digital twin virtual machine tool module specifically includes:

Build the PSN model of the machine tool;

Construct a similarity matrix, use the feature similarity matrix to cluster the similar attribute data in the feature layer sub-network, and quickly map the information between the feature-process-machine tool of the PSN model;

The scheduling scheme is determined by using the mapping relationship of the super network.

Further, the establishment of the PSN model of the machine tool specifically includes:

Construct the processing feature subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model;

Determine the mapping relationship among the heterogeneous nodes in the processing feature subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model;

The PSN model is obtained by coupling the processing feature subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model through the subnetwork layer coupling principle. According to the mapping relationship between heterogeneous nodes, each node in each subnetwork layer must be included in the set boundary, and a PSN super network model with feature-process, feature-machine tool, process-machine tool coupling is obtained.

A digital twin multi-protocol intelligent scheduling acquisition method for ship production workshops, comprising steps:

S1: Deploy the equipment in the ship production workshop and connect with the digital twin multi-protocol acquisition system;

S2: Collect data from the equipment in the ship production workshop using ModBus, Fanuc, Siemens, OPC UA, and Rockwell multiple acquisition protocols;

S3: Through the digital network port, the digital twin virtual machine tool module and the multi-protocol data acquisition module perform message queue mutual transmission, and the digital twin virtual machine tool module monitors relevant data, and saves the data to the local database in real time;

S4: The digital twin virtual machine tool module establishes the PSN model of the machine tool through the feedback of the equipment acquisition mapping, cutting mapping, welding mapping, motion prediction and digital model in the ship production workshop, realizes the two-way interaction of the key data of the digital twin and controls the multi-axis linkage mechanism of the cutting machine and welding machine production equipment to realize intelligent scheduling;

S5: The virtual and real data combined visualization module will analyze the PSN model and dynamically combine it with the real space scene to directly display the product scheduling data information on the product in real time, and directly control the product production information and intelligent scheduling situation through the combined virtual and real data combined with the visual screen controls.

Compared with the prior art, the present invention has significant advantages:

(1) The present invention adopts a multi-protocol data collection method, and can be compatible with multiple protocols for data collection, providing more comprehensive and real data for digital twins, and is more flexible to the diversity of collected data, adapting to different protocol devices;

(2) The digital twin virtual machine tool of this system can be used to realize the three-dimensional modeling display, collision prediction and intelligent production scheduling of the cutting machine and welding machine production equipment in the ship production workshop;

(3) The present invention introduces supernetwork technology based on digital twins, collects and analyzes data in a multi-physics modeling mode, and efficiently establishes machine tool production models such as PSN; based on digital twins, it obtains real-time production data through cutting machines and welding machine production equipment in the ship production workshop, and uses digital gateway technology to communicate with the ship production workshop and the digital twin system.

(4) The invention is developed based on multi-thread architecture scheduling, equipped with dual-network port transceiver hardware and a digital gateway module, supports simultaneous data transmission and real-time collection of multiple devices.

Other advantages, objects and features of the present invention will be set forth in the following description to some extent, and to some extent, will be obvious to those skilled in the art based on the investigation and research below, or can be taught from the practice of the present invention. The objectives and other advantages of the present invention can be realized and obtained through the following specification, and the present invention will be further described in detail in conjunction with the accompanying drawings.

The present invention will be described in further detail below in conjunction with the accompanying drawings.

Description of drawings

In order to make the purpose, technical solutions and advantages of the present invention clearer, the present invention will be described in further detail below in conjunction with the accompanying drawings, wherein:

Fig. 1 is a module diagram of a digital twin multi-protocol acquisition intelligent scheduling system of a ship production workshop shown in an embodiment of the present application.

Fig. 2 is a flow chart of a digital twin multi-protocol acquisition intelligent scheduling method for a ship production workshop shown in an embodiment of the present application.

Fig. 3 is a flow chart of plate cutting in a ship production workshop according to an embodiment of the present application.

FIG. 4 is a multi-protocol acquisition diagram of the system shown in an embodiment of the present application.

FIG. 5 is a flow chart of the data collection process shown in an embodiment of the present application.

Fig. 6 is a flow chart of the operation of the digital twin multi-protocol acquisition intelligent scheduling system of the ship production workshop shown in an embodiment of the present application.

Detailed ways

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 only for illustrating the present invention, but not for limiting the protection scope of the present invention.

A digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops, including: cutting machines and welding machine production equipment in ship production workshops, multi-protocol data acquisition modules, digital twin virtual machine tool modules, and virtual and real data combined visualization modules, wherein the multi-protocol data acquisition module is connected with cutting machines and welding machine production equipment in ship production workshops, and is used to obtain actual production data in real time through cutting machines and welding machine production equipment in ship production workshops. It uses digital gateway technology to communicate between ship production workshops and digital twin systems. Transmit it to the digital twin virtual machine tool module in real time;

The cutting machine and welding machine production equipment in the ship production workshop can control the multi-axis linkage mechanism to realize three-dimensional movement;

The multi-protocol data acquisition module performs data interaction with the virtual and actual data combined visualization module in real time to realize dynamic visualization of data.

The multi-protocol data acquisition module uses digital gateway technology, which is compatible with multiple protocols for data acquisition, and exchanges information between the ship production workshop and the digital twin system;

The virtual and actual data combined with the visualization module performs real data collection and analysis, data simulation mapping, and dynamically combines with real space scenes to directly display product intelligent scheduling information in real time;

Further, the multi-protocol data acquisition module includes multiple acquisition protocols, including ModBus, Fanuc, Siemens, OPC UA, and Rockwell.

The multi-protocol data acquisition module can collect the mechanical coordinates, absolute coordinates, relative coordinates, residual coordinates, feed speed and status data of the equipment in real time, meeting the requirements for various data collection of industrial equipment, and also providing reliable data for digital twin intelligent scheduling modeling. It has the advantages of high efficiency, various types of collection protocols, and accurate data collection.

Further, the client for data collection is developed based on multi-threaded architecture scheduling. The industrial computer equipped with the client is equipped with dual network port transceiver hardware and a digital gateway module, which supports simultaneous data transmission and real-time collection of multiple devices. The multi-protocol data acquisition module is connected with the cutting machine, welding machine production equipment, and virtual-real data combination visualization module in the ship production workshop, and is used to obtain actual production data in real time through the cutting machine and welding machine production equipment in the ship production workshop. It uses digital gateway technology to communicate between the ship production workshop and the digital twin system, and can be compatible with multiple protocols for data collection, and can transmit it to the digital twin virtual machine tool module in real time, and perform data interaction with the data virtual-real combination visualization module in real time to realize dynamic visualization of data.

Further, the digital twin virtual machine tool module performs modeling by receiving the production data of ship workshop equipment collected by the multi-protocol data acquisition module in real time; the digital twin virtual machine tool module then performs data interaction with the virtual and real data combined visualization module to realize three-dimensional modeling, collision prediction, and intelligent scheduling. Visual display.

Further, the digital twin virtual machine tool module realizes the physical workshop and the virtual workshop through the geometric model (the three-dimensional geometric model established by modeling software ensures the consistency between the three-dimensional details of the twin model and the physical entity), the physical model (adding the physical characteristics based on the geometric model, such as the cutting machine speed and the hardness of the welding machine), the behavior model (adding the actual running path and motion constraints of the geometric model, so that the model can work in the same way as the physical entity), and the information model (which ensures that the virtual model can read the operating data of the physical entity in real time and realize the real-time mapping between instructions and data information). The interactive integration of the digital twin completed the modeling.

Further, the actual processing time of the digital twin virtual machine tool module only accounts for a small part of the total processing time because the current production workshop scheduling plan is unreasonable. Considerable time is wasted in shipping, handling and waiting for processing. In order to improve production efficiency, the present invention proposes an intelligent scheduling method for a certain workshop by introducing supernetwork technology into the digital twin workshop.

Described intelligent scheduling method, its specific implementation steps are as follows:

1. Due to the existence of a large number of multi-dimensional and multi-relational heterogeneous data in the digital twin ship production workshop, it is difficult to carry out effective quantitative analysis. Therefore, a machine tool PSN model is first established to provide a platform for centralized and classified management of various data types.

2. Use the feature similarity matrix to cluster the similar attribute data in the feature layer sub-network, so that the information between the feature-process-machine tool of PSN can be quickly mapped. The data preprocessing results show that the similarity of each feature of the new part can be calculated to match the similarity of the existing features. Subsequently, the mapping relationship of the super network can be used to quickly develop the scheduling scheme. On the basis of real-time simulation, the rationality of the scheduling scheme is verified, and then the optimization characteristics of the virtual workshop and the intelligent scheduling of the workshop are realized.

3. Finally, the proposed intelligent scheduling scheme is compared with traditional scheduling methods, and the method is applied to a ship production workshop.

Further, in order to enhance the realism of the ship workshop production equipment, the digital twin virtual machine tool module adds a collision body unit to each part of the ship workshop production equipment to prevent the virtual ship workshop production equipment from passing through each other when the parts collide during operation, and perform intelligent collision warning in the virtual and real data combined visualization module. The virtual and real data combined with the visualization module uses the physical properties of Collider to give different components (such as knives and plates) in Unity3D software. After the tool and the plate collide, the part of the plate collided by the tool will be hidden, that is, the animation effect of the plate being cut by the tool will be realized, and intelligent collision warning will be carried out.

Further, after the digital twin virtual machine tool module successfully imports the ship production equipment model into Unity3D software, it needs to give the machine tool model specific actions. In Unity3D, different commands are used to control different actions. But the principle is to assign the number of action frames, and change the position information of each frame of the specified object to meet the needs of controlling its different actions. Use the Transfrom component in Unity3D to complete the action setting for the machine tool model. In the Transfrom component, the position (position) of the selected model part can be given to it (for example, the tool component) with X, Y, and Z axis information, so that it can move from the current position to the specified position, and realize the moving screen display of the tool. Through the rotation (rotation) command, the tool can be given different rotation angles on the X, Y, and Z axes to realize its simulated cutting in different directions. By reading the data in the database in real time on the Unity3D platform, the motion posture of the machine tool in the digital world and the physical world can be kept consistent, and the online 3D visualization of the machine tool in the digital world can be realized, and the functions of virtual reality and synchronous simulation can be achieved.

Furthermore, the virtual and actual data combined with the visualization module for real data collection and analysis, data simulation mapping, and the construction of a three-dimensional visualization monitoring system for the ship production workshop. The three-dimensional visualization monitoring system for the ship production workshop is mainly composed of two subsystems, physical and virtual. The physical system includes physical entities of production equipment such as cutting machines and welding machines, integrated circuit boards, sensors, digital interfaces, etc.; the virtual system includes production equipment such as virtual cutting machines and welding machines, control panels, parameter interfaces, and production simulations. The two communicate in real time through serial ports and communication protocols to realize real-time mapping between virtual and physical.

A digital twin multi-protocol intelligent scheduling acquisition method for a ship production workshop, specifically comprising the following steps:

S1: Deploy the cutting machine and welding machine production equipment in the ship production workshop, and network connection with the digital twin multi-protocol acquisition system;

S2: Collect data on the cutting machine and welding machine production equipment in the ship production workshop using ModBus, Fanuc, Siemens, OPCUA, and Rockwell various acquisition protocols;

S3: Through the digital network interface, the digital twin virtual machine tool module and the data acquisition device communicate with each other in the message queue, and the digital twin mapping model monitors relevant data, and saves the data to the local database in real time.

S4: The system establishes the PSN model of the machine tool through the feedback of the acquisition mapping, cutting mapping, welding mapping, motion prediction and digital model of the cutting machine and welding machine production equipment in the ship production workshop, realizes the two-way interaction of the key data of the digital twin and controls the multi-axis linkage mechanism of the cutting machine and welding machine production equipment to realize intelligent scheduling.

S5: Finally, the visual interface of virtual and real data combined will analyze the digital twin simulation model, and dynamically combine with the real space scene to directly display the product scheduling data information on the product in real time, and directly control product production information and intelligent scheduling through the virtual and real data combined with visual screen controls.

The method is based on the system and includes all technical features of the system, which will not be repeated here.

Example

This embodiment proposes a digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops. It solves the existing problems in the ship production workshop such as single data collection, communication mode delay, single collection equipment, insufficient client interface function, single digital twin function, and backward manual experience scheduling, especially in transportation and loading and unloading. To solve these problems, innovative solutions such as multi-protocol digital acquisition in the ship production workshop, digital twin virtual machine tool system in the ship production workshop, and intelligent scheduling are used to solve these problems.

Fig. 1 is a block diagram of a digital twin multi-protocol acquisition system of a ship production workshop according to an embodiment of the present application. In some embodiments, it mainly includes: cutting machine, welding machine production equipment in ship production workshop, multi-protocol data acquisition module, digital twin virtual machine tool module, and visualization module combining virtual and real data. The multi-protocol data acquisition module is connected with the cutting machine and welding machine production equipment in the ship production workshop, and is used to obtain actual production data in real time through the cutting machine and welding machine production equipment in the ship production workshop. It uses digital gateway technology to communicate the ship production workshop with the digital twin system, and is compatible with multiple protocols for data collection, and can transmit it to the digital twin virtual machine tool module in real time; the cutting machine and welding machine production equipment in the ship production workshop includes various production equipment such as CNC cutting machines and welding machines, which are controlled by the numerical control system and can control multi-axis linkage mechanisms Realize three-dimensional motion, have communication interfaces with different protocols, and output working parameters including position and speed to the outside; the virtual and real data combined with the visualization module performs three-dimensional scene display of workshop equipment based on digital twin mapping data, workpiece motion prediction and collision detection, and combines with real space to directly display processing data information in real time; As well as relevant derived data generated through mining, a virtual machine tool model is constructed, and the simulation of the processing process can be displayed in 3D and real-time. Then, the super-network technology is introduced into the ship production workshop, which can optimize the scheduling of the ship middleware processing process and make the system more efficient.

The cutting machine and welding machine production equipment 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 includes a variety of acquisition protocols, including ModBus, Fanuc, Siemens, OPC UA, and Rockwell. It can collect the mechanical coordinates, absolute coordinates, relative coordinates, residual coordinates, feed speed and status data of the equipment in real time, meeting the requirements for various data acquisition of industrial equipment. It has the advantages of high efficiency, many types of acquisition protocols, and accurate data acquisition.

The multi-protocol data acquisition module uses digital gateway technology, which is compatible with multiple protocols for data acquisition, and exchanges information between the ship production workshop and the digital twin system.

The client side of the multi-protocol data acquisition module for data acquisition is developed based on multi-threaded architecture scheduling. The industrial computer equipped with the client is equipped with dual network port transceiver hardware and a digital gateway module, which supports simultaneous data transmission and real-time collection of multiple devices.

The virtual and real data combined with the visualization module performs real data collection and analysis, data simulation mapping, and dynamically combines with real space scenes to directly display product intelligent scheduling information in real time.

The digital twin virtual machine tool module is used to realize the collection and mapping, cutting mapping, welding mapping, motion collision prediction and intelligent production scheduling of various equipment in the ship production workshop. It integrates entity data, environmental data, historical maintenance data, and relevant derived data generated through mining to build a virtual machine tool model. The simulation of the processing process can be displayed in 3D and real-time. Then, the super network technology is introduced into the ship production workshop.

Fig. 2 is a flowchart of a digital twin multi-protocol acquisition intelligent scheduling method for a ship production workshop according to an embodiment of the present application. As shown in the figure below, the implementation process of its overall intelligent scheduling scheme is divided into: digital twin virtual machine tool module establishment, PSN model establishment, similarity matrix establishment, feature similarity calculation and matching, calculation mapping and simulation, and production intelligent scheduling scheme. The specific functions of each module are as follows.

The digital twin virtual machine tool module can be used to realize 3D modeling display, collision prediction and intelligent production scheduling of cutting machine and welding machine production equipment in ship production workshop.

The digital twin virtual machine tool module realizes 3D modeling. After successfully importing the ship production equipment model into Unity3D software, it needs to give specific actions to the machine tool model. In Unity3D, different commands are used to control different actions. But the principle is to assign the number of action frames, and change the position information of each frame of the specified object to meet the needs of controlling its different actions. Use the Transfrom component in Unity3D to complete the action setting for the machine tool model. Among them, by assigning the X, Y, and Z axis information of the tool component in the Transfrom component, using the transform.position=Vector3.MoveTowards(transform.position, aim.position, speed*Time.deltaTime) command can move the tool from its current position to the specified position, and realize the moving screen display of the tool. Through the rotation (rotation) command transform.Rotate(new Vector3(0,0,-speed*Time.deltaTime)), the tool can be given different rotation angles on the X, Y, and Z axes to realize its simulated cutting in different directions. By reading the data in the database in real time on the Unity3D platform, the motion posture of the machine tool in the digital world and the physical world can be kept consistent, and the online 3D visualization of the machine tool in the digital world can be achieved, and the functions of virtual reality and synchronous simulation can be realized. Further, in order to enhance the sense of reality of the production equipment in the ship workshop, the digital twin virtual machine tool module adds a collision body unit to each part of the production equipment in the ship workshop, so as to prevent the virtual ship workshop production equipment from passing through each other when the parts collide during operation. In the Unity3D software, by giving different components (such as tools and plates) the physical properties of the collider (Collider), you can use the OnTriggerEnter (Collider plate) command to realize the collision detection between the tool and the plate, and then use the destroy command to destroy the part of the plate that is collided by the tool to achieve the animation effect of the plate being cut by the tool. Its specific process is shown in Figure 3.

The digital twin system adopts multi-physics modeling to realize the multi-physics modeling function of the digital twin. It mainly constructs an intelligent scheduling model through the twin system. The digital twin is a true reflection of the physical entity in the virtual space. The success of the application of the digital twin in the industrial field depends on the degree of fidelity of the digital twin, that is, the degree of virtualization. Therefore, the multi-physics modeling improves the degree of virtualization of the digital twin and gives full play to the role of the digital twin; and the digital twin system realizes the acquisition and mapping, cutting mapping, welding mapping, motion prediction and digitization of the cutting machine and welding machine production equipment in the ship production workshop. The feedback of the model can realize the real-time dynamic model establishment and monitoring feedback of the cutting machine and welding machine production equipment in the ship production workshop as a whole.

The digital twin virtual machine tool module realizes collision prediction. In order to improve the realism of the production equipment in the ship workshop, a collision unit is added to each part of the production equipment in the ship workshop to prevent the parts from passing through each other when the production equipment in the virtual ship workshop collides during operation. In Unity3D software, collision bodies are divided into 6 types, and different types of collision bodies can be added according to the shape and use of machine tool parts, so as to realize the collision prediction of cutting machine and welding machine production equipment in ship production workshop.

The digital twin virtual machine tool module realizes intelligent scheduling. Combining the digital twin virtual machine tool module with the multi-protocol data acquisition module, all kinds of data will be collected and interacted with the digital twin virtual machine tool module in real time, and then the interaction between virtual space and real space can be realized, and new models such as production machine tool PSN can be established. It provides a platform for centralized and classified management of various data types, and realizes intelligent scheduling of ship workshops.

The implementation process of the intelligent scheduling scheme: the construction of the similarity matrix of the feature layer super network. When comparing the similarity of two production equipment features, the following six attributes cannot be directly calculated, and a similarity matrix must be constructed to calculate the similarity between functions. The main decision-making attribute features of a ship production workshop include structure type, material, category, precision, size and roughness. The mathematical model of the processing feature is PP={PC, PM, S, PS, MA, PR} where PC is the process category, such as: cutting machine, welding machine; PM is the processing material, such as stainless steel, cast iron; ST is the structure type, such as center hole, pinhole, etc.; PS is the process size; Quality of Service Workpiece Performance and Service Life. These six attributes of process characteristics belong to different data types: PC, PM, ST belong to qualitative symbolic data, and PS, MA, PR belong to quantitative symbolic data. When comparing the similarity of two features, a similarity matrix must be constructed to calculate the similarity between features. Constructed as follows:

Two process features with attribute f can be defined as (f _i , f _j ) and the similarity between the two features is as follows:

Among them, α, β, γ are weight coefficients; P, S, C are the distance factor, span distance factor and content distance factor respectively; and α+β+γ=1, α∈[0,1], β∈[0,1], γ∈[0,1].

(1) When f _i and f _j are quantitative data, their similarity can be calculated as the distance between two values in position, space span and content: the position distance factor P(f _i , f _j ) is:

The space span distance factor S(f _i , f _j ) is:

The content distance factor C(f _i , f _j ) is:

f _i (min) is the lower limit of f _i , f _i (max) is the upper limit of f _i , f _j (min) is the lower limit of f _j , f _j (max) is the upper limit of f _j , inters is the number of intersection values of f _i and f _j , and U _f is the difference between the maximum value and the minimum value of the fth attribute.

(2) When f _i and f _j are qualitative data, there are fewer position and U _f items in the distance calculation:

The space span distance factor S(f _i , f _j ) is:

The content distance factor C(f _i , f _j ) is:

m _i is the number of elements in f _i m _j is the number of elements in f _j Among them, m _f is the total number of elements in f _i and f _j , inters is the number of intersection values between f _i and f _j .

According to the above similarity calculation process, the similarity matrix between features is obtained as follows:

According to the different feature attributes, the features can be clustered into different classes, so that the similarity within the class is as large as possible, and the gap between the classes is as small as possible. The clustering map point set is reduced from high-dimensional space to low-dimensional space, which reduces the network dimension while maintaining the original distribution of data. Furthermore, clustering the data at the hypernetwork level assigns data to different classes and provides a basis for correlations to build scheduling schemes in the feature-processing-machine tool hypernetwork.

The implementation process of the intelligent scheduling scheme: the establishment of a new model. In order to quantitatively study the relationship between the multi-source data of the "characteristic process machine tool" in the digital twin workshop, a PSN model was established on the basis of the stored workshop production process database.

The specific steps and methods of establishing the PSN model are as follows:

1.PSN consists of three layers of subnetwork sets and boundary sets, as follows:

Among them, N _PF is the sub-network of processing feature layer, N _PP is the sub-network of processing technology layer, and N _PM is the sub-network of processing machine tool layer. E _PF-PP is the intersection between N _PF and N _PP , E _PF-PM is the intersection between N _PF and N _PM , and E _PP-PM is the boundary set sum between N _PP and N _PM .

2. The processing feature subnetwork ( _NPF ) model is as follows:

where v _pf-n is the nth node of N _PF , and (v _pf-i , v _pf-j ) is the boundary set sum of v _pf-i and v _pf-j in N _PF .

3. Definition of processing technology layer subnetwork (N _PP ):

The sub-network model of the processing technology layer is as follows:

where v _pp-n is the nth node of N _PP , and (v _pp-x , v _pp-y ) is the boundary set sum of v _pp-x and v _pp-y in N _PP .

4. Definition of processing machine layer subnetwork (N _PM )

The subnetwork model of the processing machine tool is as follows:

where v _pm-n is the nth node of N _PM , and (v _pm-α , v _pm-β ) is the boundary set sum of v _pm-α and v _pm-β in N _PM .

5. Mapping relationship between heterogeneous nodes

(1) Mapping relationship between N _PF and N _PP

The mapping relationship between characteristics and processes reflects the characteristics that a process may contain, or the process that a characteristic may belong to. Let the Boolean variable θ(v _pf-i ,v _pp-x ) denote the relationship between feature v _pf-i and process v _pp-x as follows

Therefore, the mapping relationship between features and processes can be defined by the above formula. From the mapping from process to feature, we can know which process corresponds to which feature.

In the feature point set V _PF , the process v _pp-x includes a feature point set as shown below:

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 formula represents the process of v _pp-x corresponding to the characteristic V _PF (v _pp-x ).

From the mapping from features to process V _PP , it is possible to judge 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 formula represents the v _pf-i characteristic corresponding to the process V _PP (v _pf-i ).

(2) Mapping relationship between N _PP and N _PM

The mapping between operations and machines is based on which machines can be used to complete an operation, or which operations can be performed with the machines. Let the Boolean variable θ( _vpp-x , _vpm-α ) denote the relationship between the process _vpp-x and the machine tool _vpm-α as follows:

Therefore, the mapping relationship between the machine tool and the process can be defined by the above formula.

Through the process-to-machine mapping, it is known which machine is associated with a process.

The relationship between the machine tool V _PM and the corresponding process v _pp-x 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 ) indicates that the machine tool corresponds to the process v _pp-x

The relationship between the process V _PP and 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 ) means that the process corresponds to the machine tool v _pm-α

6. Subnet coupling. By constructing N _PF , N _PP , and N _PM , the PSN can be obtained based on the subnet layer coupling principle. According to the mapping relationship between PSN's heterogeneous nodes, each node in each subnet layer must be included in the set boundary. Likewise, supernetworks with feature-process, feature-machine, and process-machine couplings can be obtained.

The feature-process supernetwork is defined as follows:

φ _PF-PP is the coupling set between N _PF and N _PP , SE _PF-PP is the boundary set between the point v _pf-i in N _PF and the point v _pp-x in N _PP .

The feature-machine supernetwork is defined as follows:

φ _PF-PM is the coupling set between N _PF and N _PM , SE _PF-PM is the boundary set between the point v _pf-i in N _PF and the point v _pm-α in N _PM .

The process-machine supernetwork is defined as follows:

φ _PP-PM is the coupling set between N _PP and N _PM , and SE _PP-PM is the boundary set between the point v _pp-x in N _PP and the point v _pm-α in N _PM .

According to the PSN model constructed above, the data of feature processing and machine tools are comprehensively integrated and mapped.

Specifically, the researchers use the boundary connections in the super network to find the corresponding process and machine tools according to the processing characteristics of the parts to be processed, so as to provide effective technical support for the intelligent scheduling of the ship production workshop.

Combining the above innovations, the application process of the intelligent scheduling method of this system is as follows:

1. Geometric model: According to the design drawings of the production unit of the ship production workshop, a three-dimensional geometric model was established by using modeling software. The goal of this process is to ensure consistency between the 3D details of the twin model and the physical entities.

2. Physical model: We have added physical properties based on the geometric model, such as cutting machine speed, material size and welding machine temperature material and other information.

3. Behavioral model: On the basis of the physical model, the actual running path and motion constraints of the geometric model are added, so that the model can work in the same way as the physical entity.

4. Information model: Using the unified communication protocol, the information of "physical workshop-server-virtual workshop" can be connected with each other. Subsequently, the virtual model can read the operating data of the physical entity in real time to realize the real-time mapping between instructions and data information.

5. Through the functional cooperation of the above four levels, the interactive integration of the physical workshop and the virtual workshop is realized, and the 3D modeling of the digital twin is completed.

6. Then build a PSN model on the basis of the stored ship workshop production process database (this model building process has been described in detail above). The processing feature (feature processing uses the feature matrix, which has been described in detail above to establish a model) as the minimum entry point of production scheduling, and refines the historical process data and real-time data in the ship production workshop into different super-network layers. In order to efficiently match the discrete features of the new part, the similar attribute features in the feature layer sub-network are clustered using the similarity matrix. The purpose of this matrix is to calculate the similarity of processing features of each ship machine, such as cutting machine, welding machine, etc., and then match the features in the corresponding database. Through the mapping relationship of the hypernetwork, the type and location of the processing machine tool corresponding to the feature can be determined.

7. Based on this, the processing time, transmission time and waiting time between adjacent features can be further determined (the first step is to judge whether the workpiece has a waiting time), and through the calculation and matching of feature similarity, the processing time of the corresponding feature can be determined. According to the processing time of the workpiece, the transfer time of the workpiece between adjacent features is determined, and then intelligent scheduling is carried out.

Fig. 4 is a multi-protocol acquisition diagram of cutting machine and welding machine production equipment in a ship production workshop according to an embodiment of the present application. As shown in Figure 4, the compatible protocols of the digital twin system of cutting machine and welding machine production equipment in the ship production workshop of this design include ModBus, Fanuc, Siemens, OPC UA, Rockwell, and various acquisition protocols can run in parallel.

Among them, ModBus includes ModBus Tcp. Among them, Siemens includes S7-S1200 and S7-S1500.

Among them, Mitsubishi PLC includes EtherNet/IP (CIP). Among them, Rockwell includes EtherNet/IP (CIP).

Fig. 5 is a flowchart of a data collection process according to an embodiment of the present application. As shown in Figure 5, the acquisition process is mainly divided into the following steps:

Step 1: Run the client software;

Step 2: Select the device to be collected in the device library, and configure the relevant communication address. Click Test Connection to monitor whether it can be connected normally, and then click Save to the added device library;

Step 3: If you need to add multiple devices, repeat step 2;

Step 4: After adding the device, perform unified collection, click the unified connection button on the client to match and connect the device, and then click collect to collect data in the background, and automatically save it to the database, and output the relevant data to related modules;

At the same time of unified collection in the background, you can double-click a certain device to perform related monitoring, and a monitoring interface will be displayed at this time;

When the collection is completed or a temporary interruption is required, the collection can be ended uniformly, and if necessary, it can be collected on a single machine;

After all the collection tasks are completed and no longer continue, the connection will be disconnected after the collection is completed.

Fig. 6 is a flow chart of the operation of the digital twin multi-protocol acquisition intelligent scheduling system of the ship production workshop according to an embodiment of the present application. As shown in Figure 6, the operation of cutting machine and welding machine production equipment in the ship production workshop is mainly carried out in the following steps:

1) The cutting machine and welding machine production equipment in the ship production workshop started to operate;

2) The acquisition module reads all kinds of required data, and analyzes coordinate data, current and voltage data and other special data to the designated module;

3) The digital twin virtual machine tool module acquires data, establishes a simulation mapping model PSN, and outputs data for virtual-real visual monitoring.

4) Simulation is performed after the digital twin virtual machine tool module is mapped to the model, and the judgment and expected operation scheduling plan are displayed in real time on the virtual and real data combined visual interface, and corresponding feedback operations can be performed through the interface.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention without limitation. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the purpose and scope of the technical solutions, which should be covered by the scope of the claims of the present invention.

Claims (10)

1. A digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops, characterized in that it includes workshop equipment, a multi-protocol data acquisition module, a digital twin virtual machine tool module and a virtual-real combination visualization module of data, wherein: The multi-protocol data acquisition module is connected to the workshop equipment to obtain the actual production data of the workshop equipment in real time, and interact with the digital twin virtual machine tool module at the same time; The digital twin virtual machine tool module is used to realize intelligent production scheduling of workshop equipment, which integrates entity data, environmental data, sensor data and historical maintenance data; The virtual and actual data combination visualization module communicates with the multi-protocol data acquisition module and the digital twin virtual machine tool module through Ethernet, and is used for real data acquisition and analysis, data simulation mapping and display of three-dimensional scenes of workshop equipment; The digital twin virtual machine tool module performs intelligent production scheduling by receiving the production data of workshop equipment collected by the multi-protocol data acquisition module in real time; the digital twin virtual machine tool module then performs data interaction with the data virtual and real combination visualization module, and performs screening and comparison of intelligent solutions and intelligent optimization in the data virtual and real combination visualization module to realize visual display. 2. The digital twin multi-protocol intelligent dispatching collection system facing the ship production workshop according to claim 1, wherein the workshop equipment includes cutting machine and welding machine production equipment; the multi-protocol data acquisition module is based on multi-thread architecture, includes a dual network port transceiver module and a digital gateway module, and adopts multiple acquisition protocols, including ModBus, Fanuc, Siemens, OPC UA and Rockwell acquisition protocols. 3. The digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops according to claim 1, wherein the actual production data includes mechanical coordinates, absolute coordinates, relative coordinates, remaining coordinates, feed speed and status data of the equipment, and the digital twin virtual machine tool module displays all product information in real time in three dimensions. 4. The digital twin multi-protocol intelligent scheduling acquisition system for ship production workshops according to claim 1, wherein the intelligent production scheduling of the digital twin virtual machine tool module specifically includes: Build the PSN model of the machine tool; Construct a similarity matrix, use the feature similarity matrix to cluster the similar attribute data in the feature layer sub-network, and quickly map the information between the feature-process-machine tool of the PSN model; The scheduling scheme is determined by using the mapping relationship of the super network. 5. The digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops according to claim 1, wherein said establishment of a machine tool PSN model specifically includes: Construct the processing feature subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model; Determine the mapping relationship among the heterogeneous nodes in the processing feature subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model; The PSN model is obtained by coupling the processing feature subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model through the subnetwork layer coupling principle. According to the mapping relationship between heterogeneous nodes, each node in each subnetwork layer must be included in the set boundary, and a PSN super network model with feature-process, feature-machine tool, process-machine tool coupling is obtained. 6. The digital twin multi-protocol intelligent scheduling and acquisition system for ship production workshops according to claim 1, wherein the processing characteristic sub-network model is: Where v _pf-n is the nth node of the processing feature subnetwork model N _PF , and (v _pf-i , v _pf-j ) is the boundary set sum of v _pf-i and v _pf-j in N _PF ; The sub-network model of the processing technology layer is: The sub-network model of the processing technology layer is as follows: Where v _pp-n is the nth node of the subnetwork model N _PP of the processing technology layer, and (v _pp-x , v _pp-y ) is the boundary set sum of v _pp-x and v _pp-y in N _PP ; The sub-network model of the processing machine tool layer is: Where v _pm-n is the nth node of the sub-network model N _PM of the processing machine tool layer, and (v _pm-α , v _pm-β ) is the boundary set sum of v _pm-α and v _pm-β in N _PM . 7. The digital twin multi-protocol intelligent dispatching and acquisition system facing the ship production workshop according to claim 7, wherein said determination of the mapping relationship between heterogeneous nodes in the processing characteristic subnetwork model, the processing technology layer subnetwork model and the processing machine tool layer subnetwork model specifically includes: (1) Mapping relationship between N _PF and N _PP Let the Boolean variable θ(v _pf-i ,v _pp-x ) represent the relationship between feature v _pf-i and process v _pp-x as: In the feature point set V _PF , the process v _pp-x includes the following feature 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 formula represents the v _{pp-x process} corresponding to the feature V _PF (v pp-x ), and the v _pf-i feature corresponding to the process V _PP (v _pf- i ) is: 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) Mapping relationship between N _PP and N _PM Another Boolean variable θ(v _pp-x ,v _pm-α ) represents the relationship between process v _pp-x and machine tool v _pm-α : The relationship V _PM (v pp-x ) of the machine tool V _PM and the corresponding process v _{pp- x} is: 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} The relationship between the process V _PP and the corresponding machine tool v _pm-α 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 scheduling and acquisition system for ship production workshops according to claim 7, wherein the PSN super network model with feature-process, feature-machine tool, process-machine tool coupling specifically includes: The feature-process supernetwork is: φ _PF-PP is the coupling set between N _PF and N _PP , SE _PF-PP is the boundary set between the point v _pf-i in N _PF and the point v _pp-x in N _PP ; Feature-Machine Supernetwork is: φ _PF-PM is the coupling set between N _PF and N _PM , SE _PF-PM is the boundary set between the point v _pf-i in N _PF and the point v _pm-α in N _PM ; The process-machine supernetwork is: φ _PP-PM is the coupling set between N _PP and N _PM , and SE _PP-PM is the boundary set between the point v _pp-x in N _PP and the point v _pm-α in N _PM . 9. The digital twin multi-protocol intelligent scheduling and acquisition system oriented to the ship production workshop according to claim 7, wherein the digital twin virtual machine tool module adds a collision body unit to each component of the ship workshop production equipment, and performs intelligent collision early warning in the virtual and actual data combination visualization module. 10. A digital twin multi-protocol intelligent scheduling acquisition method for ship production workshops, characterized in that it comprises steps: S1: Deploy the equipment in the ship production workshop and connect with the digital twin multi-protocol acquisition system; S2: Collect data from the equipment in the ship production workshop using ModBus, Fanuc, Siemens, OPC UA, and Rockwell multiple acquisition protocols; S3: Through the digital network port, the digital twin virtual machine tool module and the multi-protocol data acquisition module perform mutual transmission of message queues, and the digital twin virtual machine tool module monitors relevant data and saves the data to the local database in real time; S4: The digital twin virtual machine tool module establishes the PSN model of the machine tool through the feedback of the equipment acquisition mapping, cutting mapping, welding mapping, motion prediction and digital model in the ship production workshop, realizes the two-way interaction of the key data of the digital twin and controls the multi-axis linkage mechanism of the cutting machine and welding machine production equipment to realize intelligent scheduling; S5: The virtual and real data combined visualization module will analyze the PSN model and dynamically combine it with the real space scene to directly display the product scheduling data information on the product in real time, and directly control the product production information and intelligent scheduling situation through the combined virtual and real data combined with the visual screen controls.